{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive dout (derivative of loss with respect to outputs) and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch/Layer Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_train: ', (49000,))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.7698500479884e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around e-9 or less.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  1.0908199508708189e-10\n",
      "dw error:  2.1752635504596857e-10\n",
      "db error:  7.736978834487815e-12\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around e-10 or less\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.999999798022158e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be on the order of e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.2756349136310288e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be on the order of e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1: \n",
    "\n",
    "We've only asked you to implement ReLU, but there are a number of different activation functions that one could use in neural networks, each with its pros and cons. In particular, an issue commonly seen with activation functions is getting zero (or close to zero) gradient flow during backpropagation. Which of the following activation functions have this problem? If you consider these functions in the one dimensional case, what types of input would lead to this behaviour?\n",
    "1. Sigmoid\n",
    "2. ReLU\n",
    "3. Leaky ReLU\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward and affine_relu_backward:\n",
      "dx error:  6.395535042049294e-11\n",
      "dw error:  8.162011105764925e-11\n",
      "db error:  7.826724021458994e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "# Relative error should be around e-10 or less\n",
    "print('Testing affine_relu_forward and affine_relu_backward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.999602749096233\n",
      "dx error:  1.4021566006651672e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.302545844500738\n",
      "dx error:  9.384673161989355e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be around the order of e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be close to 2.3 and dx error should be around e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.22e-08\n",
      "W2 relative error: 3.17e-10\n",
      "b1 relative error: 6.19e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 1.37e-07\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 9.09e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "# Errors should be around e-7 or less\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.304060\n",
      "(Epoch 0 / 10) train acc: 0.116000; val_acc: 0.094000\n",
      "(Iteration 11 / 4900) loss: 2.252924\n",
      "(Iteration 21 / 4900) loss: 2.128607\n",
      "(Iteration 31 / 4900) loss: 2.033476\n",
      "(Iteration 41 / 4900) loss: 1.990910\n",
      "(Iteration 51 / 4900) loss: 2.043805\n",
      "(Iteration 61 / 4900) loss: 1.959265\n",
      "(Iteration 71 / 4900) loss: 1.921205\n",
      "(Iteration 81 / 4900) loss: 1.892495\n",
      "(Iteration 91 / 4900) loss: 1.768856\n",
      "(Iteration 101 / 4900) loss: 1.829613\n",
      "(Iteration 111 / 4900) loss: 1.683077\n",
      "(Iteration 121 / 4900) loss: 1.752042\n",
      "(Iteration 131 / 4900) loss: 1.703082\n",
      "(Iteration 141 / 4900) loss: 1.815607\n",
      "(Iteration 151 / 4900) loss: 1.669903\n",
      "(Iteration 161 / 4900) loss: 1.738107\n",
      "(Iteration 171 / 4900) loss: 1.604972\n",
      "(Iteration 181 / 4900) loss: 1.648884\n",
      "(Iteration 191 / 4900) loss: 1.638778\n",
      "(Iteration 201 / 4900) loss: 1.857390\n",
      "(Iteration 211 / 4900) loss: 1.678935\n",
      "(Iteration 221 / 4900) loss: 1.756143\n",
      "(Iteration 231 / 4900) loss: 1.643596\n",
      "(Iteration 241 / 4900) loss: 1.541258\n",
      "(Iteration 251 / 4900) loss: 1.438778\n",
      "(Iteration 261 / 4900) loss: 1.718157\n",
      "(Iteration 271 / 4900) loss: 1.667385\n",
      "(Iteration 281 / 4900) loss: 1.673117\n",
      "(Iteration 291 / 4900) loss: 1.648107\n",
      "(Iteration 301 / 4900) loss: 1.744448\n",
      "(Iteration 311 / 4900) loss: 1.564542\n",
      "(Iteration 321 / 4900) loss: 1.468176\n",
      "(Iteration 331 / 4900) loss: 1.456966\n",
      "(Iteration 341 / 4900) loss: 1.738916\n",
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      "(Iteration 4031 / 4900) loss: 1.213610\n",
      "(Iteration 4041 / 4900) loss: 1.164401\n",
      "(Iteration 4051 / 4900) loss: 1.281760\n",
      "(Iteration 4061 / 4900) loss: 1.290094\n",
      "(Iteration 4071 / 4900) loss: 0.976122\n",
      "(Iteration 4081 / 4900) loss: 1.444818\n",
      "(Iteration 4091 / 4900) loss: 1.355808\n",
      "(Iteration 4101 / 4900) loss: 1.528054\n",
      "(Iteration 4111 / 4900) loss: 1.237279\n",
      "(Iteration 4121 / 4900) loss: 1.655397\n",
      "(Iteration 4131 / 4900) loss: 1.279284\n",
      "(Iteration 4141 / 4900) loss: 1.576452\n",
      "(Iteration 4151 / 4900) loss: 1.135667\n",
      "(Iteration 4161 / 4900) loss: 1.115728\n",
      "(Iteration 4171 / 4900) loss: 1.168133\n",
      "(Iteration 4181 / 4900) loss: 1.500695\n",
      "(Iteration 4191 / 4900) loss: 1.096998\n",
      "(Iteration 4201 / 4900) loss: 1.342433\n",
      "(Iteration 4211 / 4900) loss: 1.291486\n",
      "(Iteration 4221 / 4900) loss: 1.183129\n",
      "(Iteration 4231 / 4900) loss: 1.133089\n",
      "(Iteration 4241 / 4900) loss: 1.180310\n",
      "(Iteration 4251 / 4900) loss: 1.358058\n",
      "(Iteration 4261 / 4900) loss: 1.072809\n",
      "(Iteration 4271 / 4900) loss: 1.192425\n",
      "(Iteration 4281 / 4900) loss: 1.519956\n",
      "(Iteration 4291 / 4900) loss: 1.155352\n",
      "(Iteration 4301 / 4900) loss: 1.063927\n",
      "(Iteration 4311 / 4900) loss: 1.143543\n",
      "(Iteration 4321 / 4900) loss: 1.258275\n",
      "(Iteration 4331 / 4900) loss: 1.115814\n",
      "(Iteration 4341 / 4900) loss: 1.117280\n",
      "(Iteration 4351 / 4900) loss: 1.226422\n",
      "(Iteration 4361 / 4900) loss: 1.310496\n",
      "(Iteration 4371 / 4900) loss: 1.061019\n",
      "(Iteration 4381 / 4900) loss: 1.165791\n",
      "(Iteration 4391 / 4900) loss: 1.233291\n",
      "(Iteration 4401 / 4900) loss: 1.351398\n",
      "(Epoch 9 / 10) train acc: 0.550000; val_acc: 0.493000\n",
      "(Iteration 4411 / 4900) loss: 1.263450\n",
      "(Iteration 4421 / 4900) loss: 1.020435\n",
      "(Iteration 4431 / 4900) loss: 1.144307\n",
      "(Iteration 4441 / 4900) loss: 1.112442\n",
      "(Iteration 4451 / 4900) loss: 1.192322\n",
      "(Iteration 4461 / 4900) loss: 1.160763\n",
      "(Iteration 4471 / 4900) loss: 1.393806\n",
      "(Iteration 4481 / 4900) loss: 0.944011\n",
      "(Iteration 4491 / 4900) loss: 1.106497\n",
      "(Iteration 4501 / 4900) loss: 0.971352\n",
      "(Iteration 4511 / 4900) loss: 1.115983\n",
      "(Iteration 4521 / 4900) loss: 1.250686\n",
      "(Iteration 4531 / 4900) loss: 1.154294\n",
      "(Iteration 4541 / 4900) loss: 1.227414\n",
      "(Iteration 4551 / 4900) loss: 1.130883\n",
      "(Iteration 4561 / 4900) loss: 1.043215\n",
      "(Iteration 4571 / 4900) loss: 1.225017\n",
      "(Iteration 4581 / 4900) loss: 1.077988\n",
      "(Iteration 4591 / 4900) loss: 1.324153\n",
      "(Iteration 4601 / 4900) loss: 1.490974\n",
      "(Iteration 4611 / 4900) loss: 1.307169\n",
      "(Iteration 4621 / 4900) loss: 1.025476\n",
      "(Iteration 4631 / 4900) loss: 1.240466\n",
      "(Iteration 4641 / 4900) loss: 1.212575\n",
      "(Iteration 4651 / 4900) loss: 1.319671\n",
      "(Iteration 4661 / 4900) loss: 1.121676\n",
      "(Iteration 4671 / 4900) loss: 1.192146\n",
      "(Iteration 4681 / 4900) loss: 1.216914\n",
      "(Iteration 4691 / 4900) loss: 1.065608\n",
      "(Iteration 4701 / 4900) loss: 1.056466\n",
      "(Iteration 4711 / 4900) loss: 0.997057\n",
      "(Iteration 4721 / 4900) loss: 1.413904\n",
      "(Iteration 4731 / 4900) loss: 1.104649\n",
      "(Iteration 4741 / 4900) loss: 1.233721\n",
      "(Iteration 4751 / 4900) loss: 0.998040\n",
      "(Iteration 4761 / 4900) loss: 1.382885\n",
      "(Iteration 4771 / 4900) loss: 1.209479\n",
      "(Iteration 4781 / 4900) loss: 1.450412\n",
      "(Iteration 4791 / 4900) loss: 1.165108\n",
      "(Iteration 4801 / 4900) loss: 1.035754\n",
      "(Iteration 4811 / 4900) loss: 1.219323\n",
      "(Iteration 4821 / 4900) loss: 1.233136\n",
      "(Iteration 4831 / 4900) loss: 1.252312\n",
      "(Iteration 4841 / 4900) loss: 1.386555\n",
      "(Iteration 4851 / 4900) loss: 1.207884\n",
      "(Iteration 4861 / 4900) loss: 1.298591\n",
      "(Iteration 4871 / 4900) loss: 1.327423\n",
      "(Iteration 4881 / 4900) loss: 1.537574\n",
      "(Iteration 4891 / 4900) loss: 1.425693\n",
      "(Epoch 10 / 10) train acc: 0.593000; val_acc: 0.496000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "solver=Solver(model,data,optim_config={\"learning_rate\":1e-3})\n",
    "solver.train()\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dN0IIIYQQQkgkmB4ZQnnYnB4pxNWm2n7GD59pOWyKWr1BhUlCCCGEEEJIZBhpC0FK+9/8jtj+l05jpmaOzFFhkhBCCCGEEBIVOm0hXLI4YUAz9fHBiWmnfVFhkhBCCCGEEBIVpkeGkJajVfKKVJgkhBBCCCGERIZOWwhpOFqVcklb+0YIIYQQQgghYTA9MoTRkQp+7cU3MFtfiPX9SrmEo3tvTXlUgwnbJBBCCCGEENIJnTYHlnvFWE5bsSCYEukI2yQQQgghhBCih06bA2G92kxEyT1d6lEmW5uEpXQeCCGEEEIICcKaNgfiipHUF6RTbzYVZarO1CBxNco0OVWN9bv9iKkdAtskEEIIIYSQpQ6dNgfGdm1AySvG+q6L08Fm3GbHmG0SCCGEEELIUodOmwOjIxU8cddmiBjfdXE6GGXSO8Zsk0AIIYQQQghr2pxQ9WYy4vc8RyGS68olVDUO2qBGmWz1e0u5ro8QQgghhBAddNpCCKoaRsIxNDe2a0PHbwxqlClMJZJOGiGEEEIIIe0wPTIEXb2ZK/WGmxAJACwfunopVg972mbck1NV7Dh4BDfuPYQdB4/0pVAJ6/cIIYQQQgiJBp22EJLWlVVnalbnSkWeZmpX2wpc0fSEGxSFSdbvEUIIIYQQEg2mR4ZgqjeLgj/9L1jPNTs379SfLCxCFVYLlpc+cEutfo8QQgghhJCkMNIWQhK5f4VyrnTRsouGxt3ByJMpEqUibrYIXBpRurRSM6kSSQghhBBCSDTotIWg5P6LIo7g/1Xen6lFqo8LRp5MkaiiEKE1YknryNJMzVTns1IuQQColEva+j1CCCGEEEJIE6ZHOqAcitgqkgBKXsE5zVIXeTIpTJrG44/MJa0jszl9cZwtqkQSQgghhBDiDp02R5STsf+l022iIa7MasRFFMNeAatXLm/Vm+3cuBbjh89gz8R0aB+z8cNnQmvEktaRUTyEkKvkpT6UEEIIIUsHOm2OKEMtjsMWxrKhIo7uvbX1O1H7mAUjcF5BYHZuHjfuPdRyAl84UY3dB47iIYQ0CXs+CSGEEEKygDVtDvhrurJgplZviXtErT8L1oiVSx4ggIuz9Vb92Qsnqrj75opzHVlQdGTnxrUUDyEE7DNICCGEkN7ASJsDSRpsu6JW7E2/Y3MY/RG4HQePdEQDa/UGXjl5DtP7bgsdhy6SoJy+1948z5QwsqRhqjAhhBBCekGo0yaE+H0AXwDwPSnlT2n+vgrA0wDWLe7v30kp/5+0B9pLumWQ2RxDV/VK01hnanVMTlVDHS1TJOG1N8+3UjgJWaowVZgQQgghvcAlPfKrAH7O8vdfAfCXUsotAD4H4MtCiGXJh5YfXAwyL+NE04aUxr/50xkLFufOJYWLkQRCzLDPICGEEEJ6QairIaX8EwAXbJsA+IgQQgC4ZnHb+XSGlw/Gdm1AWJzLIg6pJWrXt4rBcQz2ULM5dy6Ol8lBZSSBEPYZJIQQQkhvSKOm7bcBvATgfQAfAbBbSql1YYQQXwLwJQBYt25dCj/dHUZHKnhwYjq1/QkAq0peJCXK6kwNI4+/in13bGozEJM07NZh6gfHSMJgQxl7d9hnkBBCCCHdJg2nbReAaQC3ArgJwH8XQvyplPKHwQ2llF8B8BUA2LZtmzkklDMmp6ooCmGNYkWhPOxhZjZ664CLs3WMPX8SwFV5cde0RVfHy9QPLg0jdZAdg34+NsrYE0IIIYTkmzSctn8C4KCUUgL4GyHE2wA2AvhmCvvuOcqgTcthA5rOV1wnsN6QGD98pmVMm4QRyiUPK5cPWZ0Ik6MRJZLg6qz0s2MQdoz9fGyAXca+H8ZPCCGEEDLopOG0nQXwDwH8qRDi7wHYAOA7Kew3F2Ql95/ECXx/ptZyJKozNQg0Cwv9CAFrtCfM0XBxxqI4K/3qGLgcY78em4LiM4QQQggh+cZF8v9raKpCflwI8V0A+wB4ACCl/F0Avwngq0KIU2iWa/1rKeX3Mxtxl8mj4VryCm2OhM79uzhbx56JaTw4MY1yyYMQwMxsveWAhTUJdnHGojgraTgGvUhBdDnGfnd6KGNPCOk3+jklnRBC4hDqtEkp7wv5+/sAwrs29ykmg7aXzDpKVSpnzi94Up2pWUVV3p+p4bGXTzs5Y1GclaSOQVopiFFf9C7H2O9OD8VnCCH9RL+npBNCSBwy7i7W/+j6Mg0yq0oeLhpEUoIOTJT2AEn7W4VFBl0ItkdQL/rJqarxOy7H2O+9uyhjTwjpJ9J4HxBCSL+RRk3bQKNTU1z/sRK+8dYFbVpiPxGshSt5RVh6c0MC2HHwSCs6FSVCk1SV0hTxqs7UcOPeQ077i5LOaasZDB5jloqb3YIy9oTpZqRf6PeUdEIIiQOdNgd0Bu0jk6fwzLGzfe24STSjKn4jbU9IPzqTEIdNWTENQ9CWpuqPmvnHFcT1RR9MvZG46uBWDMdAp4f0M0w3I/1Ev6ekE0JIHOi0xWByqooXTlT72mEDmg7I0b23tn2moks2/NEpm7OSpiGoi+rZxqXD9UWvi8gphy14vggZBPpdAZUsLViHSwhZirCmLQZZtQHoJl5RaF9wrjV81Zkadhw8Yq0HS7PuIFh3FTauG/ce6hifa+0ZU2/IUoP3POknWIdLCFmKMNIWg343ZAriapNu4GrU65HJU/ja6++19ZCzNQGvztQw9txJPPby6bZ2AoA9Yqc+16VOqu+qz3ZuXIvX3jzfkV654+AR7f6Fb//VmVqr7YFKa3zirs2h6ZpMvSHA0qrx4j1P+g2mpBNClhpCJmjynIRt27bJ48eP9+S3k2JyGPoBnajGE3dtxvF3L+DpY2c7tt9x0xp86+wl58iiVxDAolNoG8P929fhhRPVtv26fFeNF0BouqTpu2Ev+mBaZ5Tv5hlXJ2QpOStBwgRo+v0eMNHNe34p31+EEEJIECHECSnlttDt6LRFZ3KqirHnT1qdi36iXPLaern5KQqBL39xi1OtWxRsEbwwVG2ZzcAO+24YeTQsk4zJ1SgfVIfVBd2xBxnkusZu3PNL+f4iJI/vFUJI73F12pgeGZfB8NcAwOiwAUBDylYaSpoRxrgOG9BMT/W//KI4gC6prXl8scYRdfEfR0FzjnRCE0tZkMKlVrXfU6NtdCPdbCnfX2RpQ4VWYiKPNgfJJ3TaYjB++AzqCwPktTniouDoSpJI2wqv0DaOKPvx1+iYaury8mKN43T5v+tyjoJOSJggxSC/XFwcMtZ4JYOCJ2SpwgULooPOPIkC1SNjsBQNDGWs1+oNFBc7cJdLHrxiu5ajVxAdnwUpeUXcd8v1TiqVOmr1hViOo18pUk2U1Zlaq8+bEi1JS/EyCcHxuTpdCleF06ATYnJKriuXtOfs4RdPWRVEozA5VTUqf3aDMIeMkuLJsd1fhAwyXLAgOtJU2SaDD522GJgMjHLJQ7nkdXk00RAAVg97LZnk1cP28VYWjfWx50+2UiMbUsIrCuy/cxPG79nSJrs8fu+Wjs8e2L6uQ5r5wOhmPHHXZu35srt88QhKQpt6sZkIe7Gm4XD49/HQsycjO13+77ukseqckLFdG7RO986NazN9uWTtELqgawmhzgQlxdPBte0GIYMGFyyIDjrzJApMj4yBqbHnF7Zci9fePG+tEes1EsCV+gKe3L0VoyMVPDJ5Cs8cO6t1WJQx9djLpztEV+oNicdePo2pR2/rMGSDhva2G9bgwOjmVrRuz8Q0xg+fwdiuDVi5fKjjfMVNPNWJkXhFgfF7tnSMMeqEaHuxppHe4JrO6EcXOQxz9IpCYEFKa2pjQyOwM/EX7xmFd9J4ueQhdcjv0A9i+mc3MaXR8hyTpQobgoczyOn3JthuhUSB6pEx8U8uq0oe5uYbmK0v9HpYqbF62MPtn246obaoTaVcauup9srJcx1OWMkr4u6bKx0S/yWvmGqT8mGvgCvzC/CXG3oFgfF7O522KKIqYep2I4+/iouznY56FKVB1/GYnC6X77uo9Nn2Y6pDTENR8ca9h7TOugDw9sHbE+2bdBcqRBKiZyk6Ja70w7yRxfXrh+Mm2UP1yIxRq8ZXH7jBcdgA4EdX5q3RFYW/kbWuzxvQjJgEm3arz02OgAAwVBSR2ironOb6gtRGa1xFVSohE/PkVFXrsAHRIlAu29omctv3BeD8grHtpyFlh6Od1koxVxsHhzxETfMKjfalDRuCm8n7vJGVYAizD0gU6LQlxFXwod9IWx3TlO5n+lzC3mQ7CjpHxD9R2hopq232TExrJ1NbPZdyOFwMNZPT4pLOaPt+1CiYaT9qX2O7NmTycmHq0OBgcvyrMzXsOHhkyRomVIkjWTEIiwF5r+3K0qmkM09codOWgMmpaqoNpwcZIYAeZeIaozX+iTKu/L/thTK2a4OzoWZyWlxTJHZuXKuNdO7cuDb0u8Exjz13ssNp94qioy4pTbjamG+iGIUmx1+gPTIfx2HpZ+M075EE0huS3tODshiQ92yLXjmV/TznkfSh0xYTNVGGUSwINAasp1tRCHy0NGRMC9TRK4fNNVqjc0Z2HDwSamSZXjTlktdqSO5iqCV1Wl5787zz57aXgPrf/S+dbtUmrh72sO+OTZFEVeIcx6CsNnbjJdvNF7nOKBx77iQee/k0ZmbrHb+vW4DQiQRFdVj63TjNeySBdJ807um0FwNc5pYs5p9eZlskyYbJ0qns9zmPpA+dtpi4pkXKAXPYAGBBSuy7YxP2TEzHVnqMi8748+MVBK5ZMaQ1Jk2YJmwXI8umJGoT9TClbMadiE2/E0xJ27lxbZsgjO4lkGQcS/0lE3b8aRg7aZ5jl/Ho5rr6gmwt2vh/37+9qletWFJuozgs/R6pynskgXSfNO7pNBcDXOaWQavtSpoNk6VT2e9zHkkfOm0xcZ0Qk8iT7LhpDb7x1oWuO0ZhXFcuYXSkguPvXjC2C8iCyqLToURNikLgk2uH8Z3zs61/7/6Z63FgdLPzPm0TtouRpXvRrP9YKfS8pGmoTU5Vjc5sMCVNN640XwJL/SUT1ssuDWMnrXPsaqy4zHW1egP7XzqND+evNr5XwjU7N641Pg9RnoN+j1SxbpMESeOeTnMxwGVuGbTaLtfj6YVT2e9zHkkfOm0xsQk2pEUeHTYBtIyMA6Obse2GNW2pdFly8fKHbXVbDSnx19+73PbvF05Use2GNcZatZ0b1+K1N8+3/j07N2+csF2NLL+S6P6XTuPoWxesx+E/h2kwfviM8T4Jfm7aLq2XwFJ/ydiOPy1jJ61z7Doe17lONwco5VjTgkKU58A0joIQuHHvodzXe7BukwRJw+FKczHAZW4ZtDk+yvF026lkdJ4EKfR6AP3K2K4NKHnFts9Eyr+RN4cNAD570xoAzXqvG/cewv6XTuODD+e78tsuffD8UQ0VSajO1CBxtS2B/9+murzqTA2jIxU8cddmVMolCDQjfSZhkEcmT2HPxLST8yqRbqpgGi9L00tgcqrautY7Dh7paJzuup+l8pKxHX8aKYJhvxEFV2NFN9dFwaYQG+U5GNu1AV6hc5ZtSNl6nh9+8VToPdpLRkcqOLr3Vrx98HYc3XsrHbYlju7ZiupwRXlPheEytwzaHJ/n40nj/iCDBSNtMdGtmgbrhQaRP//OBXzznYstOf5uRNjC6tiCKKMzSTsGZRu6rKxNTlUjpYlWUk6NLBh63Zl64AXRvQRU1NB/fV3S+cZ2bcDY8yfb2jUo5clBwVYHZlr1TitF0PYbUc+x6ypucK5bVfJweW6+7RqXvCJWeAXtIoitKXtkQlbGdJHCrBriprlPKsQtTdKKvqYVAXKZW9JSKs4LeU5bZnSeBKHTlgDdRLnthjVtxk03nJpusiCBhZT6p9nw9yeLmoaqjM4kESi/fowyqKoztTZxBTV52tITg6T5MlCRRJ1BXPKKuPvmSugiQlGIjlXZYK2TH6d0PtecTAtZGrFJ9h1WB6ZqPf11l3ffXMFrb55PJUVQ/Q6Q/EVuUnnUGV/Buc6lRQbQvA8/s25VR6q3eg6iXIvxw2ecejf6n/ssRBPS3qdufw9OTOOxl09HUm11/a0snqteKQ4OAnlSzXWZW6IoFfcDeXeM8nR/kN5Dpy1lgg/YyOOvRpIChEW+AAAgAElEQVTGJ02+/MUtrfNoU2EM4neK0qg7DBpUykHyG2quzmEl5ZeBKZLod8TUIoLpPCxI6aQW6Md2vOOHz3T0eKsvyNxIuydVdwwXGnkDNV8ar6qzNJ3PqCmCwfE9uXtra1ymBvAmdGJCEuioCzV91+ZcBbMP/HeEAHD3zc3v6q7F8XcvtNWdquNxfZb9kcIsRBPS3qfpebs4W09VeTWr56qXioMkfcKchEGraQPoGJH+gU5biuhEL3rVn6zfUUbw6EhFGxFQDHsFLPeKzv2iXBHi6vU0fV8ZauVhL9Qxf2r31kTGuc4QN70kG1J2GPEmx02Xmhf28rWl85m+W52pYXKq6nQOwhyjJCuiSdUdbcena0qu9p9GiqCpXxoEWhGoqMawLgKYxAEJGj+6PoVy8Xdfe/O89lr4nUi/I+eSJh2MZGdhYKa9T9v38qjsGpybbGJO3VAcJN2F4hiE9A46bSmhM6h0ed/EDZ3xqRwPv/E2W1+AhMD929fhtTfPY8/ENPa/dBpCNFeq44rDSAmjER4cp04cIcjY8ydbxxLmkJlWpYMRiOFlRVye63QogzL/D794SpsqaUrVDItQzs7NGx0w23ddnQmbY5S0B1pSdUfb8dnuFSV/n6RuwtQvLUgUYzjrVfM4+9c5kSYFSqDZ/N3UlzELAzPtfYY9b7ZzFSXlMI1rrZubXMY9iNGZpUqea8AApuFGgeeq/wh12oQQvw/gCwC+J6X8KcM2nwPwFAAPwPellP8gzUH2A0lEL4ieWr2Bh549iePvXsArJ8+16gN1Rp1/dd5fR5gk0BnmsAHNiJzLdvWGbB1LWHNrk/PgXwSwGUu68/Pam+fxxF2bnSbosAilLW3L9l1XZ8JkxBaFSNwDzWZwx22m7kLFF/GM+4KMYuC6bpv1qnnY/l1THm2COlOP3mb8m4uBGdVwSdtoDbunbMquUVIO07jWUd5zQcVBRmfcScuYzsIo73UNmO2YmIbbiel88Vz1Jy6Rtq8C+G0Af6D7oxCiDOB3APyclPKsEOIT6Q2vf+CKYTY0pHSKWPYqCzVK+qvpWILOTBb9/96fqTnn7Qdfyjp1SpMDpv794MS0cRw6/C+WVSUPXlG0K1AWhNE5DouS+Y9Ft29lcLukkAajvq6oF2USwyvKPa4zhnUv76xXzcP2rxNC0R1n3PTSMAMzjuGSttGqvqfrd2m7FlFTDtO41q7vueB+8x6dyRNpGdNZGuVZ14DFdTSYhtuO7XzxXPUnoU6blPJPhBDrLZv8IoAXpZRnF7f/XjpD6y+60WybDC5+Y8hVqj8KUVe0/S/l9XsPabcx3e+jIxU89vJpbZ2fyZnwv1hmanV4BdFKe1MS8yZsUbLqTA17JqbborD+fQcNbtdm6rptbaSlVBjEK4i2mjbTmE0v7yfu2uwcgXUZq2k/YeIutrYpJiVUV6M/TDQljuGSttGq9pdlumMazqbpPVcueVi5fMi4315HZ/qJtIzpfjXKkzgatmdiKaYC2s4XU5b7kzRq2v4XAJ4Q4v8D8BEA/7eU0hSV+xKALwHAunXrUvjp/JBE9MJGsSCw/cbVHZLZZLC4rlxqvVSSOGzlkocP5xdSXdE2OZFFoa/lm5yq4kdXOp0sU7+2x14+ra3VGl42hKlHb8OOg0esrTNm5+atYjDBkfv37UepKQbl+oP1hlGjbEn68tnS0VTKpdrOZoyYXt77XzqN6X23ZaLK6ZetP7r3Vu33dM6Pv22K/3hMnychb4ZLFGcwTsphUmfTFDHbf2d4awIq9LmR1j2Zt3vblSSOhumZWFXylmQqoO18MWW5P0nDaRsCcDOAfwigBODPhRDHpJR/FdxQSvkVAF8BgG3btg2UDxI3bSqMxoLEse9cdFIoJPngge3r2mrwwlDNl5M6/cWCwP47NwGwp4P507BWD3ttvaB0q5EmJ9L0uU72HwBWLhvqeEFOTlWN93V1pubU7uHibDN6Fkx7tKF7mT0yeaqtNrIhJZ5ZTGc9MLo5NOqlI6nDbHrpCqDDEVJzz56J6VZ6qrq+pv3M1Op4ZPIUDoxujj1G9dsusvUuq90m4z4Loz/PhoupF55Lqm9WMGKWnLA5OK17st/u7TCFXhdHw7SoIAT6MuqYFNv5Yspyf5KG0/ZdNMVHLgO4LIT4EwBbAHQ4bYOOMipMhmbJK7T1cHKlIaWTwyaAvnLuskgDzBIXyfFKuYQDo5udjXwhmj2rVHQnCQsLV3uu6V5Ek1PVDkXMi7N1PDgxjePvXsC2G9ZoVyNXG+4pUwTJ9NK9pHFiH3v5tPWYXBdA6guylaLl8p2g4TI5VW1z2BQSwDPHzraiPGEOm3oG/amXQFP6Po6R62J4Be8z/zFcnK1j7PmT1nlBHV8S48VFth5wE4vpBv6IafC5zoPhYopc+tGlEQsB7JmYxvjhM7GcqSRONQnHNAf71YXTMqbzapSH1aUlcTRMiwp7ItRYJ0mjjPPdLNM2beeLCzD9SRpO238D8NtCiCEAywDcAuDJFPbbt5hSJeccowBxkQB+dGU+UsShV5S8Ip64a3NbvVEeMDmSLg5B8IVoExhQCAATf5HcYQPCHUpTBAxoGu6H3jinXY0EZCTD1vbSDb6g0lxgmKnVsf/OTaHRbt3YbUIfEm4RdHVP+196wehdVEdFN5eolg47Dh5pCajYnMl6Q1oFc9TxJXlZu8jW56XGRufkqvu7krHh4mqguao0qlTffXdsSuwQ50VNbpBrj0xzcL0hW89BWsZ0Xo3ysHnAxdHwv1NXeIW2fekWFVz7lCZ5BuJ8N+tnLuweSCqOlaf7aqngIvn/NQCfA/BxIcR3AexDU9ofUsrflVJ+WwjxxwDeALAA4L9IKf9ndkPOP6o2Jrhy33CQhk+Ki/x8HlATrc3Y84oCP7N+NY6+daFr4yoIiaC/6xUELs/Nax0vk7EXnNS+sOVaPPP62Q7jeUECCyk52KYaM0VYbyyTAxWMDgugo9bLj+mlG0wBjZtGbIvQmnrS+b+rG3tYncf7MzXr71Y0UbVVJU97z0RxVIJp137nOdi3zsalWt0YMVXHl4SxXRsw9vxJ42JRmFiMa+P1MFyMCZ3RqJ5hU+1dGkQx0KK2d0jDIc6DU50Xx1GNpVs1lMG/pRXNzGNUNKwuzcXZ/HD+6jvJ1oJGYXME/dc5ilJykDjPTzeeubTvgTw9o0sRF/XI+xy2GQcwnsqIBoTX3jyfqwhSt/l7H1mGC5frRify4mwd/2piumOVTCHQXH185wfdLZrWZa/aHGGdsaeb1HSpd2lz3y3XW/+elsKpRPP+1qFegLV6o+Xk+HuUJRXqqRhSZBTBnnRBR6chJV44Ue1IBww7N2F/10nY22oaVaTMxSC0pV37z7MNf5qQbssodS6mWivTDR7WUgFobz4fF1djolcCDVEMtCjPqmuPQR3+a2m6g+Kel+B9snPjWrz25vlYgjm9jsamZZjarmseas3SIMzZdUn5tjkace4RkyMItM/ZpnnU5RmI8wz2o1hMXp7RpYreYiaJyfND1w3+7oM5FAr2yM8CgFlDjZ8/mpB3qjM1jDz+Km7cewg7Dh7RqiFm6bAVhcAD29eFikm41DJ4Rfs1U5hqAR5+8VTrmjWkbEtrSfpM+Pf1xF3mY60uRh5UdEzXbFzVWCnGdm1AyStaf9emAvnwi6e0192ESnGUi/879tzJtntocqra8R3T+VPn2YRS7RwdqeD+7eugu8Kzc/Pa3wziv8Zq7OrYTYsb6nzv3LhW+9vA1fQw9Rs7Dh6xngsdYb36FCbjOGujOYqBZrsf/YjFbeMcU/BamohzXnT3ydPHznbcN8Fra4vGmu6HOPdL2Hdc76Wo7Ny4Vvt5saBX1u03TPOD//zq7u0otXZxHZ3RkQqO7r0Vbx+8HUf33orREX1fNx0uz0CcZ3BVyYv0eR7oR0dzkKDTlhGDsmqWBH8Kw6BzcbbeeknZ6rTcXCJ3KuUSFqTEKyfPhRr9TqtgEgjxtQHo72+TofPQsydx495DKISkb9ooCtFWLzY6UrE6UcpocF05VY6g2qdKNa2US63ftRnStXojUn2erg2B/x7SGbSmOUWlfKqx+8/y6mEP4/dsaZ23A6Ob8eTurSgHjAKVYhRm8JqucdixV2dqeOFE1eocqF5KYUaf7fu2/Soj/fKH8x2LE90QaDBdv4IQHc9t8H40IQHjvRl2TC4Ga9zz4rLvKA41AO39EOd+0X1nz8Q0Hpk81drG9V6KsqgAmDMUPrK8U1nXlSTjSfv3XJxd/70t0D7HupDmoouLo+H6DMR5Bk2vxASvyszp1aIXaZKGEAnRkFXfNtLfSKSrmqmiWv5UPKU096+encaCbK+3q4SkXbnURJqaN5v2q4417jF7RdHmeCjGdm3oUGJzRfeCCcv9V38LqvhFwUWBFNCnm5jmFJXyaTN8gilLOqOgVm/gwYlp7H/pNISAtvl4ktXUWr0BIWAURbmuXEqUeuPaoymswboLceqdbNcP6EzB89+PI4+/alVwjSM6YbuWAoh1XoCm+I5rhkRwDC7vTf/9kFYdkV8ldnSkYlRbLXmFRGmTUZR1Xeh2fVHY77lGYZLUWaWpimmaM4pCYEHKSM9AnGdwxrDYZfo8D+RVlXSpQKctI9Iw8JJSEE2DJcsWAF5BXwdG9AgR33mJivJl/C/WJI4OoBfyUDLWmeEbql+qPa7zm+QFowxFV6PUKwhcs2KozTlw/b7O0AGAh549GalYXmdo2QguALjIcbsiFyO5uttvdm4+kVBKlB5NpgbrLsQ1lINGHTQOrD8q4Tf+bv/0tR3iOjq12tGRq73wwqT/TY7J6mHP6bzoHNfj717A04u9DV0ILp4Ez5Hp6Q7r4RinjsivomqaVmrzC9pr9tCznTWZuvOTdu+0btcXhf1elr3h/OdzVcnDCq8Qe9FFYZozokT+/ER1RsPOVx5VGvOqSqojj+cvKUL2qE/Wtm3b5PHjx3vy293EpTmwwnUVPgpqJbYfasNItijBlGBz16iUvCLuvrnSEhXQKW6ljUrnizvmOCunJqI02X5q99aO35qcqlqVFhUmNcP1ew9ptxcA3j54e8fnUeagsLG4HrvJMQPs19I0B7oqO+pe0ra2IpVFEY8o94XpfEZRn5ycqloX9EpesaPNw2dvWoN3flCzjld3fUxG6NbHXtVeg3LJw/Q+u9Nm+p0P5xvG6647xjDj2HSuw96Vtufd9jyoZ+jGvYciv4v9x2M6Pzpl2yROgmmcprkgKWG/F+X+A9yN6qj7tRFHICcrbMcFIFWHcqmR5j3TDYQQJ6SU28K2Y01bxujynL2C0NZUfPamNanXPFVnarj84XzKe21n5bLwgnnSe/zS6tP7bsNTu7c6iR0EqdUbbaIC3YgcztTqiZzML39xS1sBehKCNRmmVguVcsn8WyGnTImHBJmcqhrnCNNqdhoF4n457ifu2txRExfE1n7iUq2O6X23aeu1VM80PwJXBRzi1O+YzktQDCZp7ZyK/rjsI0zQQpe+9423LmBs1wbrfRxWU+Q/f6bnySVVz/Q7Noftge3rItcx6d6fLoubDSmtQhhhz5CtftREMEqqOz9K2TZuPZdpvK6fJyWsLnP88JlWfW3Y8UWpR0xLGMYkkDM7N48nd29N5f0QBVt9X1ZiOL2mWzWYg3r+mB6ZMTap2eBntga/SYhr7LpyeS69ur0soo3kKiqNC3Bv4NuPqEhPVs2S/WkwphU9Uwqmrcm5YuWyobZUNzVPzM7NG1e6ozY7d2kY79+HnzCRofqCNNavqX3Z0tT884AE8MKJ5ovdH6UIpiXq0hb3TEzjszetwYXLcx2RK5OqaNzaOd2YgvjTe6Pi0gQ9TEDDJUrqolwXdSGgKESouq0O3fvTdu50KdPB6zo6ou+j6n9mTWlzth6QwNXzYrsOafbN6nZ9kUtdZlh9rSJKamdaioWmd54SYjr+7oXMom6mqKLpfhhElcZu1mAO4vkD6LR1BdNDGfxsj0P9m6qRybJOrZeY6iyyxJbK1WvSdmJr9Qb2TExjqChC0/P6mQXZ3iIgDXT1dMopVH3hXF72Li+NmVo9Ui2aUhHUsXPjWm2d0Re2XIsDo5tDjfmgEejq7EvZmeYHXG0vYDPAdQ7V115/T2uQ73/ptNERUhGq+7evwysnz7UWsMJqpWzXz3Q+/WPSGZ5R0mpNhN07thoZ1+t2efH62J6bqLWNYT0kbQTfn7b0VFdD7cDoZmy7YY3xmbXV7Wy7YY22rhRoj9RlVdvlp9v1RcHfS9KMOopRndb5tD0/tXqjzZFP06F4ZPJU5H136x7qJlFrMJPUpA3i+QPotOUKpxehgLYofVDohTOaV4cNaKqVXZlfSHWMEhhoh03hFwgA0FbHt3rYw747NrVFy0x1fquHvY5nLqj498Rdm431TMEXj+vCxK//obuBXy55xmbdJplx9XnQEFtV8ozqkYD7SqVyaIPnVa1qh0UtgpjScMNSZyWAV06ec25BotRXj797QRsdMp1PP7pzlEZkO8zgsEVeXBYFgas982zGUVR1ZP95NDVndzXObMdoct7jqsXaFlttEa5uRsDiRu7iGsT+37vRUF/rMkdEMap159OfNu1KmI0VN/puY3Kq2hHVddl3mvdQXgQ5ojjqSaNyg6pySSGSHOG6EqtW+ZlK2H16cc69ggDE0nC0skD1nQs6vqqVQFTFOx0mEQrdM92N6+lXrTT9SlyxAhdhE3/Bty0yogxtF0GbNFtluPLU7q0A3NPzFDoFRpOATBSCiw06TAZaVFEs270RXORYPdxMqTS1JVDPhuvzECYYYDrGRyZPaZ/lT31iJb5zfrbt/kmaOh1UMwwudAD5VdhLS6TBdE+p1Osw0ZygIJOpvQvQGa2KM+Y40W6/yEqc6xkmfPPk7q3G/SZxtvyZIUG7xeW8ZeHoRRFxSkvwKa/PYBBXIRI6bTnDf5O5XJkCgH5T3O/XNgG9MBoVAr1JHe023U5VLXkF1FK4Gf1Grv8ZNjkiUerJsqIoBL78xXYDyZYCaqvh07U28K/Guyjc2RT7XOqJsqDkFQCIyL8ZVGCcnKpaVSx1KMGq2cD9mUQ1z9VgtRlGSZQRoziOUYwzRRyl1KT1r/2mUgfYz5PLebA5BK5OuGoV46/v9QoC4/fqnbY4RrwpqqvLqrAp1yZpDWCb18olDx/OL6R+77g863Ge8SzGZdqvbZHrnQyUUXsN1SP7lNGRCo7uvRVvH7xdq64WZAFNJyiPlLwiHti+rjmJtyFaK7P9RK8cNqD5MrlSX0hdXTRvdDtVNQ2HDWjvq+NXJzPdM5dq9cgGado0pGxTa/OPXf0daFd1U0ZQrd5oKelVyiWM37sFU4/eplU2NKX0BQUvTAIYQgBP3LUZB0Y3dyitucwjXkGgYwpaJGyOrdUXYjmJQQVGm8jUsGYCFwB2/8z1WL1yuWZM8RTQRkcquPvmilUBUWFLIYqrjDg5VY3kUMURDIjznWA/y6hqdv2oUmc7T2HnQUXI1LX039eVcgnXrBjqyCLQnQ+dIFN9QRrPW1RhCZM6JYCWerL/Xr1/+zqtUunOjWsTXWObeq2uh6TLfsMUGF1SsW33gO14k6g/2tQyg5jmKZf5a5BhTVuOca0byGvU6u6bK3jl5DntxHyl3tCKFCRh2Ct0rEoPErV6I7XIEEkPv3Kja92SepFXIgo6pI2/rsI2diX44V8VbkjpJPZiaujuF7yYnKri0hV9FLlc8toEIoKr9aao38XZOgRgVOoseUWs/1g2599vqIU5Kx/Od45PAi0VOx1xnJPJqSpeOFENXXxS59uUWhRHGVFdpyjEEQxI2vw9Tg1THGei1ylbYefJdh4ee/m0NrV79bCHo3tvda5zi3reotTATU5VtWIx/uMy3av+FEylXBumFmrDVI93//Z1eMaQlm/br4uoiWtNoQlbW5Ok6o9hNZjq+TDNU7b5Kw/PVtbkNEZDAPeeSC6UvGLXozSvvXneKBBQqy+0VlzSoCgw0A6b4kp9AQ9sX5f4WnoFkcp+ekleorV+5UaXl2VQsCBOr7w0UYZQ2NhnanXj6usjk6dw08Nfx/q9h3DTw1/HI5NXDfTRkQquWdG5PqgEL5RBb3oXz1hSgnUrt+P3bsG+Ozah5BWN0a2iELj75gq+8dYF6zHHwX99XZwVkxFSnamhbLjH4zg0LgsKXkHgC1uuxdbHXsWDE9PaHlpReoOpVfkHJ6aNv23qWxpHMCCN5ymqQxz1fASjP2PPncTI469m3rfKj8t5Mp0HU4q++tz1fETtMacbs+4+UefY9FzZru9rb57XCoaYojsuz6Fujnpy91YcGN0c+RyEiZq4jivs+bL1KcwyqhzM9tBhshmj9P3rZ+i05ZzRkQpWLk8WEFUGSrcN9LAVz9GRCsZ2bUjFKU1T0yEvzoCO68olbLthDQoJUwTqC1L7guoGad2Hw8uGYjcIL4j0msL7XyJhL8uiEG3pICplrZfOszJI4kohqwa1ykhqSImnj51tc9xMjld1pobHXj5tdSZszoBq6BtsOh3moCzI9O7/cskzpvskVYz80ZV5Z4cmLG3JJQLVkBITf/GedrFNGWdjuzZ0jEnXDN7FAAOA8Xu3YPyeLR3nEIBzGpY69j0T01g+VMDqYa+1rx03rQk9bj/BKGnYGFydCQDae72+IHFxUTBI9RZcH/i9tBsS+x0JE3HnA9fzEeW8BcdsS60Le+biRJhUVoHrWP3Yoj9Rz4Etzdo/dpuqpktDd9O4ojjCce7ZsGsXdm76LU05DkyP7ANcVv5sqob33XI9XnvzfCaCJcuHCs5S2kG2PvYqfnilnjvJ/eFlQ7j84TzmcqjW+L0fNuXI06DbaXkmCfi4qHQsAMaieBMLEpibX4AX0q8ubMxBYzUspXlByo6XZa+cZ4V6EdtSBQWA4WVFXJ5zd0C+9vp7rT5wNmVIm7iObSXdn6Lz4MQ0Hnv5dEtZ0aWfWZSIikmxt+QVsf/OTW3phHsmplvOTdJGrvUF6azEZ0tbcjXyFySwYHkeWscT3ETzFReHtVIudfRGA8zHo2t+DLTL78/U6ih5RTy5e2trnyZVSR3K4HWVHDe1zPDfB+oauAhJBdPejr97wdpUPi4qTc0kDmEyjsslTzsXqsVX195xcXrMubQ3sD1zLhEmF6Vb19S7sHso6jmwHZvfGTW1JXEV9zGNy7WlRly5ftvxhQnkDGoz7SB02vqAsPxzpdz1zOtntSlGh944l5nq4IfzC7EV/+IY7t1QnuxljVEY/ZoBWhBNYyhpc2E/6kVh6psURn1Bhka4lGM4fviM9n5duWxIa8CFNd8N/kYvKXkF/MRv/JG1VlLCzcn105AyNFXJRjAqqTA5A6r/G2CfM72CwOzcfCRHWa20qzpddS+sWBQRMTmSaURQL9XqbUqUOsKa1qa12iyhv7eVgESUfn4lr4idG9dq+wuajsfveClDcIVXCG3Ye2B0szatTIcyeKM0AjY5QH5jNc41cGkqn7R+J6rjsP/OTVrVx/13bmrbZ9Seb2G41iuZnn3TfOLH1tsrylgVLveQbb+uPT79ddW2GlqXd03wN/2LH4C9P2GU49aRpCZ1UJtpB6HT1gfs3Li244WjVnz9qw+mlURVkJ/Vav5HVzRX2NKIntjYcdMafOvsJdQXBq+p+KCzIJuLB2kKz/hTQOKmoYU9E2rCN73slEpg8EW3/ZOr8Y23LnREZcZ2bYjdbDsrXIVt6gsyklJtUYjY18UmL20zPPwpfDonftgrtNLRolKrNzqadF+crWPsuaYTo1u40t1fJkn0goA2kqnq2oK90Ya9ApZ7RWsvPnWu0lwYcE2RchG7CAoq7Flsau463lq94SwS4WoQhp0z9bnOiTAZq0myI1yayieNvkVxSOJEyJISJXKTRJ4/7WNLEv3RHbOq//TPHUrUxL9oYEIXEfMf686Na61RXdfzYxMy2XHwiPGc2jJVwu7xQW2mHYROW85Ril9Bh+3+7etwYHSz836yTL+6VKvj7YO3x+qRE4Vj37nYU9l9koy0HZOnj53FoTfOYd8dmzKJVglcfcmYUgOvK5c6+g1VZ2odz4FAU00VQMeLWOcHqahO0qbfaRMl0nvfLdcb1dGCqNSqYAQrSFiqJXA1Uv7EXZud03tc0S1MmdQp/RSFwIKU1sbL+186DaDzHpNS39Nqtr4QKr6kjDRXhyVJ4/egQTi2a0Oos6LLskzrnteNxyUaH3bOriuXjAp+WfQQdO0PGkf5Mi6uTl5UNT/T9mGRm+D37r650pFGm5a6YRSSRH90xxyWLm1bJAs6MDqn0CZy4hIZ9B+fab7ROV/BRvUrvILWXrDd48FSCSWaoiLcg6IiSact5+geQiUHHSTLaJqNsGiEIun4XF5cQgCf/eQaHM1AFY5kiwBQKAg0IuTaXpytR25a7DoWv0GmQ9Wz7X/pdKjhrp7Z19483/E868xu1fcqLqtz0Ij90BvnnK/LF7ZcixdOXK278qc6+uuSXFPcHn7xFJ64a3NH/caelOpBo6Lq4RQ6w8c0tplaPVakJqhSqnMqlhUFVi5vb4oO6JsPu/6WYnSkgsdePt2V+zAYfTCNB4DVcffXqOoyXFRKp8m4dXWw/PtTDoauJjdqU/lep1r7iVrXZNveFrHSfe+FE9VcNDdPEv2xZXeY0qVt1z94Pky2ZdT96ghbIAk63LqaVBPVxWuuq+v117YH+4wCg+G40WnLOVHC671w2PwTkG11pVzyMDffyFyWv1zycOw7FzP9jaVGtxYDJJpytssj9tvL0mGzoerZXA3qqBGeJBGhXjtsUcZQLnlaZ7ZWb+ChZ0+2/u3qsKnv6lZkk/bxSkKwwW9WYxOL+/KvwEdN+zLVcPpRDoptRXvfHZsyi0D5WblsqC36oBoi75mYbjtW9R9TVoh6pienqpj45ntax8wWCWxI6SLmslYAACAASURBVFT3KYCWYMkzx87iunIJT+3eCgAd6Wrq2VDnu1IuYXZuXvt8haW/+a95MLohRPOZVb9TXvws6NBHuYei1DXZtrdFrKL8TjDFePWw1xIvioJrBDHqc+ffrymjwBalswmpuKYwmvYbBZcFEpXFMjs3r71+tgWQsedPtv1O0PELixb2M0L2KN1s27Zt8vjx4z357X7C9HLRqQBlnZ6oUMIjQTUfnQqVjW6IigwiSRQ7o1IA8FGDWlhWVBaNlShGOukPdJGEJ+7abI2WlryiMV0mDL9jsf2Tq3H6/Q+6ei+b0M3fk1PVxMqwrupwYdy495D12VMp+sEIkK52yNToOG2eWhRMMKkh+sdlOj4B4O2Dt2PrY6/Guk9KXiG0RlS9N21jDDoXwW0AvSCE/xht50H3/TBMtZifWbeqVbpQFAL33XI9DoxuDj3HaozKSTHdHQLAk7u3Go/FNnf4bRRdijHQjK6O37MlkiBK3Jq5qPsNEvY7un2YymlM9qJpjo57bEnsUtsCqn+uc/kN/32XR4QQJ6SU28K2Y5+2nGPqt6H7vFuNej+6otmX6P2ZWqs5LhC959SC7G53qrR/TQB4YPu61gppt5ibbzbY7gYLyF5gJsj7M7XEMvh57rW3VFHGg+oLp/pHjo5UjE2kgeYqadzIob933NG3LuTCYQP0q9xJV4GVKmbUXl66fkphK+sSzZYOLn2RRkcqWOjC4rBqpOvSrymsqXHc+8RF1Gd2bl7br02NURnepl55Dy62Erj75oq1Z5ntPMQRCKovyI4IYq3ewNG3LnT0aNz06B+HNoYPNkM2cd1ilMjUo802d/gbLI8fPqNNY683JPa/dNq5p1hW/cBM18Q/X/rvER06G0wCeOFEteM7pl5s929fF9oLLwpx7dJKuWS9L/xzqEvUcFBUJJkemXNMdS2vnDzXsXKiK8RUaQ6X5+Y7VshsufQ2dKpVx9+90CaF7UK3RUXu374uVWEHVTT/h9+qdrWGSBlMg0rUHlo6fnRl3vr3sGhlGimhq4c9zMzWsarLkcq8sqrk4YUT1TYDT9WxhV2vMHpVzws0oytrVi5vpZm5XOu0DQix+F9qDnKt4zDVEd19cwUT33zPWqsZpiLpkuqVJsqgdSkp6KXSnO09oRZCwxwql7qtXvatujzX0LYIEbi64OxynP5rEqwDnZyqOkVEVXuES5btoihxJj2vptTKsMberrWBugVPXXpgt1RAXVIlg6jrbvuOfw4NSy33L2h1Q+00S+i05RzTgzxTq2uLMU3KPqaJwpa6EkW1Ks+pbP6UDeWkpsnluQZm5xqRelglZVBVNOP00NIRJgxic9hKXhE/tnoF/vp7lxONYerR21riGUsdryggBLQr1Lo+VJH2vSiDnXW9rIlafQGzc/OtfkZhqTqmhuFJVuolOhUfXeo4TFGDV06ew1BROCljBlGKqn6nSHd9vUK8/dvwL1bqxqWwGayuEcosiLJgpbu+UWqisi6lUC1C/AsqKuoT9vu6ukw/UUsxZmr1SAurtmcniSKkTWzF5niY2kj4m7crojiVaSpl2girJbUpYppSWv1zqG4RRt13KmgRdUErr9Bpyzm2BzlKYaXu4QxrfKsUz1xeq912IQSA4WVFzM41jC8nXX2Hiwx1HCSaRexAd9IJ4zY0zzPBybUXFIXAZ9atSkV9dP3eQymMaDBYuWzIeF2TLkDUF2Tqxn9U/GqXY7s2GOtsdA1+XQ1Ql3qpICYDzq+0piPuHOZfIbcdj6o1eu742VSVfv2qcX5sK+3KyVFOc1qNyKOiWoxEUaD0X18XR9m/YNANgRjd7aoWeU241GXq0kvDkDLaQoHu2ZmcquLC5Q+125tKWfzYUitdW1L40TkgeWgybQoSmI7xh1fq+MKWa43ZY2HiMbZFmB0Hj3TMZ/0sTEKnLefYnIykaQ4uL9ZeKa2FIdF0Wp5crCfTrbKoSTSokpUVYVK1abJ8qIAr9YXcRTfjiqQIoCk93uM0woaU+EaO20X0q7Nuu679ekxBlCFwdO+txjl7QcoOQ8G1vujbv/nz2rYHNqGWVSUPOw4eaTNkgGwMdr9Damut4DfKH3v5dGq/b1pg1KWOjj13Er/24htt0dkse63pUNGFYHlClEUMvyFuq4ny9wj0339ZLGC6YBMdCktRnZyqxlrYu1Sr48ndWzscAECfsqpT4hx7/qQxm8alRYstCqauS1TRnqAD0usm0y6tHh5+8Y22BagF2Sw1efrY2Q6nzDUa6N9O2Xw2kZo8tceIQqjTJoT4fQBfAPA9KeVPWbb7aQDHAOyWUj6f3hCXNrZeN64rJ1FzqBWzc/O5NqiUJPiClB3NeP1pGH6FsyydAlUo3A2irrh3i4WYN4uSQ88DOb3dATRFgC7V6qmOURl1WdYd2aIHy4cKALr37GSJmlNNC166OdvFeFDNxw+Mbsa2G9a0GZ4rvAJu//S1HUqOXkHg8tx8W61OFj0NgU6FubIlFU1Jfaf9vJuOS5c6aorO2qTGyyUPlz+cjxXVDTqUAs130crlQ6Fpe0pNV6fU6TfETffRgpRa1bzRkQqOv3vBWOddFM2+md1K+Qc6e4kpwiLDLihBE+BqRGZ42ZDTuQWaCwy2c+EqhmGbF8IWPFx+O41atahN0f2EtWAYHam0tXIJcnG23iHpHwXXzIV+FSZxibR9FcBvA/gD0wZCiCKA/wvA4XSGRfzoet24rpzEzaEG8tHrKQz1ctU5MWnUy8QZy1Km12lqg07aiw5BmfGsIg22Z6NWX0C55HX87nDEfn15QBkCphqL6kwNWx97ta3/lc3BUVyem2+rYfZHsy/O1juib0UhsGyogMtzbs1zk+BXAW39TsgPpe2wxUkdNREUfmjuv4i5+Uas+c0m+uV6HrbdsAbbblhjNaTjpMUdGN2MP/xWteM+AYCGBD6ybKh1r2Y9s+t6iQHR69d0qMwbUyNudX2CEWl/lDrsGXVxAlyiYKbraFv4Cv52klq1qE3Rg7jU1IXZSvWGjJW+6NpepJuRx7QJddqklH8ihFgfstm/BPACgJ9OYUwkQJKVk7RzqLOgWBBohLwMo+T5++mWI1XyCljhFfvC0SX5xpaykybBPou6+oE0cKmL1cub589hK5c8zM03tM5kUOnu+LsX2haN1DnwH2t1ptYSUrGt4vuNGN2cHvxmQ0qtIZ4FSgV02w1rWveQTakvC9K+V5YPFbDCK7Q1lnZNJRz2CljuFdu+q85LnAijMpqfuGuztdYrblrcrOU+USn/T+7emjjSZSMoLOEnTnuCICrz5tAb57T20Gtvnm87tzrHxYYAnJyAoC2nmprvWRQV2blxLS5/2Kmkq/rh6eo/bedOHUvw99S9qZq3++1Kk8340LMnO5rV63BZPHCx596fqUWK+IVpNADhAjf9gFNz7UWn7RVdeqQQogLgvwK4FcDvLW4Xmh7J5trdIazBpUtzy6wRaDo9plX1csnD/js7o41O+xbhq75pIBZ/i4EmkpRyycOH841MnZbVwx6mHr1N+1LM0jgbBLyCwDUrmsIqyvio+Fbn1fmL0oag5IXXqKo5O6zxdRq4LKQFidrstpvEaQnhFQTG790CIJpcufq9z960Bu/8oNb2bCVJT3UR6Ag+z8oo9wucBBdrXK6VStHMSiW6XPIwve827d+6cb8H7aE49+47mhRUm9PhGkFcPezh9k9fazz36tzpfguIVr8ajDCbUM9T8F4yHVcwffqRyVOh7ZdM6cgPaBqFT05VsefZaautVxQCbz3x+dBj6xWuzbXTcNqeA/BlKeUxIcRXYXHahBBfAvAlAFi3bt3N7777buhvk2SYJmTdC8A2ea9cVuzayq0O1cA6at+fZUWBuS7m5JN0cX2JkOiUNT3FeL6jo5qGb7thjVWoIClqzs6bQ6QQQOYRmSRUFuX0V5U8/PBK3WmBzSsA8wvppJWWvCIKAoneo1EiBTrRGoVaeFB9JIN9XHVkWd+unCYd3bjflfMRN/NIZ0+FOS+ux1UJadGgnjvd2ONcs6gL3UGHDHCribPdn2HjfmqxvYr6LV1bAB06xzovuDpthbANHNgG4P8VQrwD4B4AvyOEGNVtKKX8ipRym5Ry29q14fKoJDmmrve6cLqtc/3luQYe2L7O+lteUcQfaAgqNWhs1wZcVy45pz2GOWxeUeCB7etaEyNxo1jI7lr7EZCtdEGSHkoIIUjeHLYs7jLXfbpuJwE8c+wsfv0PT2Uq2jC7WNdmm6ejoNIy02JVycPDL57qqsPmFYTT/KCM6id3b8XK5UPOhmw9JYcNaD5bSRc+lWDTwy+esvaTm5yq4mlLVKy+IHFxsU5tplZHvSERNqVHMf69iO8HUz3Y5FRVmy4Yl3LJM9pDbs2+Cx3zgt+empyqYsfBI7hx7yE89OxJY2kK4K5e+P5MzbrtdeWScexxnOyomUn+Y1LHr8RUnty9tS36fuPeQ9hx8Agmp6o4MLoZbx+8HU/t3toSWgKa6cVh9oW/Lcf44TNODtug2HiJnTYp5Y1SyvVSyvUAngfwL6SUk4lHRlJhdKSCJ+7ajEq5BIHmjWtSaFLbmlByrCbqDYmiaD5slXKpzRlSn8elupjfPPbcyVSNgpXLhnBgtFkr8M7B2wfmwY5DlEtUQHNyzZrZ+gLrBDOgH2LPXkFgKMVbTKBptJUdjPyiEPjsTWuc9y0RP4JS8opOjoe/F5ya04F4jm255OGaFUNtc3YSSl5R2zwdaJ5Lde7TXthbAHD7p6+1OrFBEYo8RgGj4jeUdcRppbCw2Mcs6XpcUTTTSnc4Pj+mmix1vdKqry15Rey/c5PRHgpzopqCMj/W1jZo9bDXIeRUXSw1MS0sq99xVS+UAAqGZ1TV0vX6nlb1Z/7jV4sLj0ye0n6uFh1GRyqY3ncb3jl4O945eDtWr1weuvilzuHkVNXp2PtZeCRIaHqkEOJrAD4H4OMA/g7APgAeAEgpfzew7VfBmra+x9YUuIDmi9JGFop0YXVvSfbrT8vIUkFv0IhTJ0IGnzTuiyxqRJ8ypBD1ArF4kpQwQJSFiaIQ+PIX22utogg1ZfHcPrV7q7Feyz/HpiHdHkT1dVLpWCs0SpJppCbmDX86ajANzfYOD91vwjpwW7qe6ffuv2WdVhAjrftEPTO6FD0XhWlb24U46Y5H996a2Nbwp2Vn1crDeSwCGPaildCYajRdahiLQuC+W67vuB4mdHVweSPVmrYsoNOWP9J8ofobh+YZUy56XusyyODgFQQgOntI9TNKgEGnctZrwvph9YK4DpTu3ullPeI7B2/HyOOvas+vehf4DfJf/8NTqTpQ/lqVvNb8pU1TsGhB60T0qmm2Iq7asyLte1lXM+cihuHilCkbwtVR9jePtgnH6NA1So97vyvHx/Z7WWKqY3Q9nihzp4uIT6+h00YisRQjTF5RYPyeztU3/0SKLqlPkqXH6mEPP3ntR3DsOxcHqsdfpVzC+o+Vcum4DTpZquWanF5Vj2JKYwsqUbpka0TFL9ARx2FRkZRXTp5rHcewV2g24U5pUcUrNGvk0qBYEPjoooJpx9+EQLEQXs9t3HdChystbE3Og86qCxUHp0jhv58Au3qocj5uevjrkc6b33kLYnIAdY5OEnVNgWa0/4MP5yOrxSal4ouoJlG8dMEmdJMXuilEQgaANHqhZE0KpRdtzGsmqWBedg7eXWRAuThbx9G3LqRmIJW8IlYuSy5QkZT3Z2p45p/+LB7Yvi6VeiniTpJbqVzyjNerXGoamMHaMa8g8MGH89a6o6AxmEUjC1UrE8dhK5c8HN17K7bdsKatafnsogpJ1Npdk9jFNSvSE1RqLEjMGKLGDSljO2xesRl90Y3/U59YGWuffqLMBqrJeXAcqi7NL17hxyRiUZ2p4eljZ52iOH4BjbA6yFUlDzsOHok8j1+crePBiWmMPP5qm6jM5FTVeJ5UHZxf8MRU7+aCEqLptsNW8orYuXGtttYNQEfdYVJc6wf7ATptBIC7klEaxFEtK3lF3H/LulQL2aVEhwpXPzivhCjU06AK6v/NL/Q+b1+9IA+MbsZbT3weT+3eGllNjnQXZQzfd8v1WnW8/Xdu0opaLRsqdN3gSxvVCFw399cXZKQ6agG0iV0AzYhRrd5IPTU3C0N0fkHimWNnsXyogNXDXptYx/d/NJdo3yWviPsjLOSo39WJhoyOVIyLuMuLwujQuaJEXlzsgZlaPVF6oRIYUnbI+OEzxtrQsV0bnAVP8ooSb3ntzfPWJt7AVec5ieMW1ny836DTRgCYXwDlkuf0wPiNR5sSWqVcwvi9WzB+z5bQfSrVMTVZHxjdjPF7trQm8TRW8YMqXK7OqwCw46Y1iZ1I9WIsdUGJkQwewdd1WO+mrNGpdI2OVDB+75bI9/iwV0hsfJFwlBEFAC+cqLbdUwLA3TdXWveVv+3K+zO1gRD2UO++NBYuJdByKlRrhqyM6ssfzqeuxinl1ejLlfpCy2geHanEcjp17/Avf3FLaMsKF7U/03hm6wuJFSerMzXc9PDXu1br5dIKQN1b/b6wPLxsCKMjFeO5bUjZoTI5tmuDU5R2WeB5WD3saUtg+pmhXg+A5ANdY0n/Cqut5k0VtCp1nrCmkgpbnrhue+DqCxFo5nKbUI1UVXGvTWXIP0leVy45TdQSwDffvoig2Rys3whj6tHbmpPS8yedv0NIEH9qSbdrUlYuK2J2rhHa9HduPtqYlg0VMb2v+Xz0WlhhEKk41OxIAK+9eR5Ac17f/9LpxAZxFjVtcfE7B65zvw3/AmfWxvVMrd6S6M8i2FmrN7D/pdNX67tj8NHSUEfdlvr/OgEOW21TdaaGPRPTeHBiuiutebodwfK3AjAJnvi361dUewAXIRHlzB7de6vTO8CfFizQdOyVMzwojhuFSEiLsC72flXF4AMXlPn3v9xNBbcmR9BWoOvHpDKkJIeDY3/o2ZPaidhfEJv0pV0QzRz3i7N1p0mpXPJwqVandD7pW5STqP43WPCfRGK+XPJS69NEmkRtyRJVwj2M4P3Q7dYhOgU+AKksDjzle+8kkd2PQrnk4Ye1em4c4SBKmj5Mct1vf6wqefjhlXomzmg3CCqmBkVudNhaAcRpLdBMbBBOjaeDZLmwElVZXM0/SdsarFxWxL/5BX2P4jxA9UiSGTbpW1PEztTQO8xRtDE5VbU+yBXNSzk4NlVfl2b/N7UithRkp8MQAIaKokOBrZur7QJAOYdy74R0m+CCmIsBmOV85hUErjGoIGaFrm+cev+8v1gnFJd3Dt6++J55o6NXXFb0Q79M3UKqn0FTr35Kc6y2SHXQRjLZRVGj3S5tTlSblnd+UGv93uzcfGbPZNRspDTnH5NieB5wddqYHkkiYwrPvz9T06aEqBB32IQUldGRinVl1J8y5k+r9K/mXZ6bd3LYorwY+z19IU0kgJXLhiAEMDNbz6RxqssY9t2xiSl2ZElTLnmYevS21r8np6pOz+DOjWvxTEg/q7jUF2TXF1P8CnzB9LukTE5VMfbcyVjRjUFGAm02QJB+r9Pys+OmNRgdqTg37i4KgbtvvrqIorOLJqeq2PrYq5GzDkwKo/7f9pe2KGylJ0mJ4rCp9OU9Kb276w1pvQ/7ATptJDKmnGu1UqlDfa57Ufodqyi45EUHHUa/87bj4BHnSXCo4J5msKpPGot3i5laHSWv2LbSmtYk7IISrMljc2XSG3oR4ek1l3xznZqHXXjl5LlU6r3ywvd/9GFr4TBNR2HYKxhT8LMkjV8rCOBnP5lttKU6U8ONew85pwumhVcUWFYspCaaY7M5Vi4r4t5t65wadysaUmLim+9h4i/ea2WkVGdqGHvuJB57+bRzqUUQf6mG7bdfOFHFthvWtNlfvX7e/X3ylPhKWuPp90V1pkeSyNhyrk0Pl8rXtqVWqr4orpE419xuU2PFqE0pXRvXekWB3T99vVX8JO+ol0SwXikpadYPRqHkFXH3zZW+viaE6CgvZgy4NIFWohVRn+cHtq8bqGcnbfGOgmimfaXViLubmMoXwsoPliJeAVi53LM2YM9TuqpXEIBA6H3pt7+A3qar6uw13XjinueiEPjyF/OXIsnm2iQzdP161KSvZI79+BW6XCNxwYaL/l5qwe+EYWpnELXPjauNU29IvPbmedx9cyVSM9E8oQ5VGXZprR6rBrgXL3+IbrbuqtUbeO3N8y1p8zzSrfPRr/fkoKNrCuxyrWZqdUC63T/KUYn6PD997CzmG4PhsAHpOGzqdKu2FP3osAFXe2PduPcQdhw80nrXjo5UcP/2ddbvptF2J2/Yjqi+gLbo4Gx9oeO65+kuUA5l2Nzgt792HDyCPRPTbb36uolcHIcfnc0Z9zw3pMSeiWk8MumWaZA3GGkjqWOLlIVF2lwicQqXSJtKg/LXU6mxRElhiIoK7w9KSlEeKAogiV2kVvBGHn91SaXFLTWiFrrnESUYAsB5xTtPK/y9pCgE/v6qFZnPvU/t3grA/fr0C8HIW7dUMPNEpU/e3Wk980rx95ljZ7Wq4CpNMypeQWB+QcYeY1BMDkiv9UiYOE63YaSN9IzRkQqO7r0Vbx+8vdWYU5E0EudHty+vIFqrQ+WSB4hmrw5d1E71H9KRtGnpqpLX97nTeeMjK7xE12XV4or4vjs2pd6Utht86hMrY3+3H483Lh9ZPtRake1XriyKI0Wpu6LD1qQhZeYGd6VcGohGxzpq9QYenJjG+r2HcNPDX+/1cDooiKbYR1YoRyGsAXiaFET0TIvVwx4+e9OaxPNcyStqHTbgaq++H12Zd9pXueS11B6LoqkDkGReCtpsSugnjVpIJY7Tb9BpI13FlloJREtl1O1r/N4tmHr0Nrx98HasXD7UkbqghEkAe3rlymVDrbSXOFyem0d52Pz9klfEasvfs0JN8P2Y1TJTqydKQZqp1bHp0T/GnolprFw21HLu+yHFRwB46/zl2N/v19QtE7YrdqlWby0adaMJbxaoeYoLP/nDZZFxUOi2qIoLRSHwl+c+yGTf6tqOjlRw982Vrr0bPrrCay0qmlAjqZRLeGr3Vuy7YxO+dfZS4sWaz6xbha+9/p5xPzO1urMI26VaHTs3roVAeveO32YbP3wmVWXWfnx+qR5Juo5fwTGIqc+bekna9qXSMvdMTFtTE9WDatvmUq2ZThl3RafekJCyOXZT83Cgu6k1/lQDm3ywp+mr1i0Emg5lVtltSkHMr2iZdSuAohDY/snV+ObbF2O/cCTcayq7jQBQSEmoxhUJszCQf4FHN5/0Cyq9vB/StKKQNGWqlyh5dv8i46BdnzTJImU3qzYRKkVw/PAZPDgx3dV040shdoYuTXDHwSOpzGvfeOtCqseZRcmJstnSftai6hrkAUbaSK4Ii8SZ0AmYmNbICkI0w+y7Nhi3sbUvcGWmVsfyoauP2OphD0/t3oqpR29rOZtP3LU589U8gWb9hT9V1faSGL9nizbKWBDZp9lJ6B22LCKTagUvKwEQgWaz3bee+Dye+ac/i/F79ee1n6mUS3j74O348he3dDWdCNA7bF5RdCzw+J/BfkLV4A4aSVOmeomSSFfpWrZ3yFKnKIQ12yQvlLwintq9FWO7NuCFE1d7F3bzHi1YzpWq5w/aQDb7JMo9meZxZnXOrvOlXKaFLRiQZxhpI6mSRuNsWyTOhK62QEK/0teQEg+/eApP3LUZ929fpy2+TUOWXqBdaeqKpon36EgltGdZAUB4+28zEuiIRpomV1WrAaCjSWxRNFsZuDQMTZOiEC3VR1cJaldJ87Drq4uUuiIBjDz+apsIzv47N2Hs+ZMDka7oFQRm5+ZbvZc+s24Vjr51oadjqjckHpyYxvjhM9i5cS0mvvleXzY69hsUacvTLzVKXgE1zdwbl2DvzxUp739QaMjuN06Pij+ClVbkKg4NKfGjK/MdWS4Czeb2OruqbOk7OkjThX8uTNPu8EfM+wk6bSQ10mycrdu3zRk0rTr5e435US/eo3tvxbYb1hj3HTe1SucsBl/2CluKTcW32q7GuKrkQSwKrLig6nrCeq8INK/ZjoNHMDs332Hs1hearQwWLBNnFiklDSkjOdAlr5iKA1EUArV6I1GPOnWNVKuDrFNukipsRkK0H1+e6gOqM7XU0nTSNvpdUNHfx14+TYctIfOBEygA/PgnVuKvvxe/RvT9mZpvPu2dwxa37x5p9h48MHq1BUyWaa4uCy+6xSWJZrqhfy5Tjbf79Xrb3oHqPvZvU6s38NjLpwGkq+ppE6LLM3TaSGrool0mRyUKLs6gyfGpWNIc1eemyJ76rCUI4Nhc2/YCNalg6pyp1cNemwOpG2OYfL1/lcqmdOafJG2TYlidTZLXiG0yd52oVb2gmuST4Nqj7lMRDMCsXrMCQKHLUvd57k8UhouRq+SuAWRe9xjk8lyjI9LdT+y4aQ3e+UEtF/Veuvv0bxI4bEDzfZMH5ci4ffdIu9E+OVUNXVBT7YPiRA/Tfoz7dV4AmirOpnp6dR8Hj+7ibB1jz5/E7p++PrXsiTzMTXHoz2R/kkuiyPVHweYMKmytBJI01/a3L3B9Ly5IaVSts6lgBuudLs7WjY3FFfvu2GTMXxdopu7smZgO7WnnOgWuKnmZySGn8Rq6Ul/A8XcvdDUt57sXr/z/7N17mBT1nS/+96d7amZ6ZoAZYCAwgCBRLkaFyCqGTeIFJdGYsGokribxJHvc7G72RNdlF/O4XhI3sg/ryu7ZmN96svmZizEYced42/UGronGRHBQwk1FERkQhstwmemZ6en+nj+6qqmurmt39XXer+eZB7q7uutb1dXd9anv9/v5lGxdThRQ9bXJSmnnPZd5ZpY05tKWKy10NZ+Y7ToUx8srLqrY+V6FXlxavmRWRfUsU3DGqJIZK57CLY+84XpMmDNTV2tG2krRG0/k9b2QSCqsiQvzMwAAIABJREFU3bgntO9FQW4R72rAoI1CU0hw5MZPMOiWwMSrNlzYjCGWQda5dH4HmhtyO76N4LSzqzvzA7No5brMl83S+R2OPzYK2TXqwnBsIB0MGT0QlSaeSOKh3xanYLrbOquhbAClGQlt3E66zXM7q/WKbKG0AjL0dPfGMe+uZ6uq99UPAfCJmWNd5wVT9TASl7n1VFozN5a6hlslKvTXLt/PTphDkVmnjUa8YgVHfoNBp6Le+WaktPKT+c9c5yXoOp1OIo3hoObMmOYeuEKu/AkQKCtjSgF3Pr4l89wgjCxdxSyMCpQnNX5SqYJOcimYQvb05WdNwqKV61xPHLp745i+4ilMX/FUAWuqXlFJDwUrRKEFcDtaY7h+4bTM91u5P10drTFct3AaXt99dMQG8iOR9ffW7re9SQt+Kq1FJPTMxUbtUeOzU6xriUpfR7k/k4Wqxt5yUWUaC71gwQK1YcOGsqybiieM7JF2r2lXuy2fwKvQdrjNM4mK4N5rzs67TU5DGJ3m3xipgL0SjHhZvWxezvO9xvcHnRBsvVppPU76h4YrPtOYG6PGTzFq1IQhKoKUUiWvp1aJCskIOlIYJ2PlOlJaYxo23XFp5rbX8O5iM75ry92OQrQ1aejtT/A7IE+tMQ3NDXWZ36wLZ7dj/fYe7NUvpgZh/Ka3uszvyocxh39yCX6Pmuujmbqn1cr4XFcCEdmolFrgtRwTkVCo8knX7+c1AQQKBosRPBrPv/PxLTlftGEEkU6FxZ1OMM2JVADkFQSZh4GZn+91YhLkCpXdF6P1OCk08CwmY0iUkVjBGtAKgOnjYiUflhlESim8t/JydHZ1lzypRqVxOsY6WmPYdzTObI04OYqhXAFKbzyBzq7uzHdEua+IG+v32w4tkj4pr6Rjyfg9GIkBW5BkUU5644nM736+2WljWgQDiVTmPQgzYDPaZfz7UJEvIFZ7wFatddrY00Y1pxQ9c8UICp1e1yndvdtVIj9BkNs+cbui3Nakoam+zvcJnQB4b+Xljo8b22wXEAGFZe0Ki/kqazXWZTIfK04ZRwXAdQunYe3GPVW3fYUyjtGROhzSTADct2weAP91EYvFyAZbaM3MQgXpaWtr0nD5WZPw5Bv7Qj8pJ3vFLqUSBi0iVZ1cqBYYpResI38qgd+eNgZtVHOcTkpL2RUeZlDnFIRedU5HZniG3TqsbTAP5/Bqk9NQ0GhEcO8Xzwbgv4Zd0ODSCNLMRamtwyqrdYhSvqxFV4My1yTq7Op2PBnvKHMPS7mIAPddMw+3PFJ4/SNjfkt/FQe+xknNhvcPl33Ir/FdV852rF42D0vnd+C2zs2e7WiNaRgcTlXkqAEro+fH64gfaQXewwoCzcMVq30KQK3QIoJVX8x/GkuxcHgkjUidXd2OX4ylGmITdpFxu+GLF85ux9qN3a7rsBuqag7kjMxJbjXqzENBjave5uXd2gR4D0GwK+eQSCk01deh6/ZLc5Y3tqkYwynLUaDWz8lBGL0Nazd2Y8EpYzPvndM6yz0MrVyUSl+EWHhqW8FF2evromhuqEN/Fe9Lo4Dvqi+eXfagLZ5IlrUNTVok87nxU5C3mnrX/Paoj6SADUiPOCi0p9R6sXIGe/ErQiKlcPMj6SkClRa4+cGgjWqKWwpXpyyUYQ91LEaRcWsAtmjlusDrCBpMes1PdHr84d9+gKRSiIrgqnPcXyPf2n7mQDZoIBNzGN5Y6oAtIsD5p47F67uPOgafEUEmUF46vwMzVjyV1xXgeCKJWx55w3M+W7nnMpVTPJHEq+8eKfh1zHNfqlkipTKZYvNV7T000Yjge1eelbk9Ui9qjDR3Lz0T67f3FPQ5vnB2e9ZtP3PFqTSMi3RA9QVunnlKReRHInJARH7v8Ph1IvKm/veKiJwdfjOJ/HH7UbXr8TECGad0+mG2Icwf/HzW4adIuROnOnHWZdZu7M4EP0mlsHZjt+u+DKPw+Wp9/o0fMS2KxgqpsZNSwOu7j+KqczocyzZELTmhC6l56CcovXB2O5YvmeVasL2WjcQkDW4KDT7/+LxpgUqKVJK2Jg33moZRdXZ1I+KRQz1oCRWqTJ1d3QX/Xv/s1d044/b/yvxmWoM4Ki+/5z6Vxk9xiQcBfMbl8fcAfFopdRaA7wJ4IIR2EeXF6aS2NabZXlEpJJAJ2oZCi4z7ea2IiGNglW8w6TewzWdfhlHbb+n8DscTpdaYllMrrzeEeQVaRHJq4vip42cVTySxfnsPXl5xkW3glkiqrP1X7MKu67f3eBZsL1QhgV+tB42GmBapiQK+67f34I4rzqi6bbl+4TR03X5pVsB262ObPYP66xZOq8rtpWzLH30DrSEE331DycxvZrmHGVOuauw59wzalFIvAXAc5K+UekUpZYwpeRXAlJDaRhSYUxBw5+fPsF2+GL1ixSoy7rUOIN1T4BRY5RtM+g3G/OxLa48dANxz5ZlZAU9jHoVK7U6UjPfdWnC90OC5rUnDqi+eja7bL8287t1Lz8SmOy7FrpWXY9fKywMVPPdKJ26+31zYtRiMdRXr9Y0CxfkEXzEtiutMxZajeq9HtFgVZMuoUc/salxwaI1pmYsE1aS7N46b12xCQ10krwLE5WKdu2b3HWjn57/djZv07a2294pOSiQVBqsgkQwVJswL6aUS9py2rwP4T6cHReRGADcCwLRp00JeNVHwmm5O48wL+TDnU1eu0HXYFUy1znFzqgPnFUz6DWy99qXTnLqrzunA4PDJOWZH+hOBx5sH2ed2+8GNUZg6yPsYZB3G/vF7LBYzGcvk1hg6u7rRNzhc0OsI0oGHtWD7hbPbM1ksjbmPfl7Lbd97Fb0PSylTdhvJlOyyrs689em8hnGWKy26QnqYpRYVXL9wmuP7Xklp263fbX4v4hmHRy3MafRSaEbbSlfN2V/JW03XaROR6QCeVEp9zGWZCwHcD+APlVKHvF6TKf+pEpSiplspOCWosNZI85t0xbycXUAI5GbH8tqXTjWOnLI2hlmiwa78QZCgIZ996Cc9uHn/5HMsmtsyJqahb2i4oBOpJi2CREqFcjJ2/cJpeOjV3VnHpZG+fc1rH/haR1QEO++5zHOfO5X58GvRTPeEMEYGz7ue2FKytN3WY8PY/nzfmSYtUvYT0bYmDb39CcdtMFKkO33nlIrx3TMSS4zEtAjuufIsz6RF5uOpSYtgYDhV1UlnRqqoCBq1SNUXyw7CqElqXECsBCVN+S8iZwH4IYDP+gnYiCpFKXrFSiFoL40ba/Bgd/Jkd5XKa186Xa12OjmzDqvM9z2y6+EzJ0zxopDO1mlsr58MnEZSFjtGj4K1wGc+x6L1/ezs6vasNRbTIhh0OMEK66S+ozWG9dt7ck7O44kkHvrtbvg9H7/2vKmeWU/dynz4tetQHPdceaZjkfcBfb/cccUZofduOoknkrjz8S1ZZTcKUe6ADUj3ILY1aa51NDu7uj0DhmK7cHZ7UXqyq8Gw/sXgVQLFfDwpCAO2KmPUXHW6YKhF0u9/Lb6vCsCTb+yrqKDNr4J72kRkGoB1AL6ilHrF74rZ00bkLUjPWFg9hm49YkGHCfp9XbeetkK3Leh6naQzT0Z8FW536vmJiuDea/Iv7OnneLDbX8aVxQWnjA3lRFSLCCBw7C1zOjEPwigI7vT+GfMgwwhozL2pbu9dSimMiWkQAQvlhsj4PAMo+Pg03qdCzjVbYxqaG+pGVA+bWUce6elF4OtijBYRtDTWobc/gTExbUQMJa1U5guHdr8tAHDrY2/6rudXiEK/z/MZXr162byKuUDvt6fNT8r/hwH8BsAsEdkjIl8XkW+IyDf0RW4HMA7A/SKySUQYiRGFIEg5AnOCCnOmxHy+kJx6xFJKZSX0CMopQcu15011TdxSaIZPtx4+zZJSPwLnlN3xRNJX4Xa3np+UUlkZ6bzKKJj5PR7sjoX7ls3D3UvP9J1QwcqahXPVF8/GqqvPziQFsSZdONKfKDgRw0Ov7nYM2IBw66EZc/nm3eU8zNJI8tMbT2AgkSoosUZMi2L1snmhJHyJCNBQVz1JPqzM31X5Hp9mxvdUIan3e+OJogZsbU1a5v03PlNuGXALTbYTFcnKdGus18ne3njg/ecnYLMmcWpuYKngcurujWP5L9/A/O88i5v13u3rFqbzTdy8ZhNWPbMjtBI5XsdwId/nWkQy7Q6iGlP+++ppKwb2tBG5czphDXOuV6nX69RT5NaD5FZQ2pgD49b759ZTY50DpkUFq64+Gzev2RToqp1537gFGubeGuu6vXoPw3hf8inO7adX06lt1qufMS0KgaqIoXoGY57d2o3dJRkKZwR7lbQPisHPlW+jR8v4DHsFSubl3ebaLl8yC3/1yKaChnYF7Yn3y/iO8dNDbnz2Nrx/OO+U8dbPr9/5yn2Dw6H2gi2aORa7DsWz5hUzDf7IUcxEQ9GI4NpzpwY+nqzz1cuppHPaiCg8XpPfi11bxCnL5IWz27Fo5bqC5v45zalzm2vndDInQOZ+t7lldpkQtYjg2EAi56TOqIvmtM7WmIbB4ZRrBk6398c4QbI7GbJm+7QKozyFnxNjMwFw1Tne8yCd2mDM3bMOufmrNZtQ7JDFbTirWUSAp97cV7K5S7UcrAkQaPio+eq63VzCnNcXZI4hu7l+xmfxrie2FDwXp1iJUKy1F80Xq646pwPrt/fkfMcund+Rd4BjHkzgZ76y+Xs1TK/sPJx5b1m3rPZ0tMbQPzRs+7kXAK0hDJl3kkypvI4npvwnooL4mfxe7C8au4QYF85uz+qJcAqS/AiaVMQuiLQ7ubMGPU770siQmHI4d97bG8d9y+bZBq5GvT+39gcNjKzrduI32Yzb/g1a7kAht2ZVkLY59QLm03NgnlPpdHJgMLI9At5zpPqGkiMmc1pRr3br8zULmZOm4N7GI/0JLP/lG7bzKY33fOn8jrInMvFiDEszb4eRIMkcuBnB3dL5HXn3/PUNJTPf1U7DT8373O8azBdjvD6PQV6XgquUchlOx4ACMFBhCX2qNeU/gzaiCuI1p6NUXzTWnq9FK9c5zisLErR5ZQJ0aguQHSj56YV02peDw8r15Gdya8wzk2PQINMvt4DcT509a82yzMkhst9Tt4Dcyk9PntNQpwtnt9su7xQIup2YGnOVAOeAvLk+ir//o9yhnIWkbbdLulLKmm1hUkgfM9Zj6OPTxuDlnYcLeu26qIQSLJl7Z2GT3MJpvzfV12WGWgfVWoaEGHbbEU8ks8pkGJ/fu57YUlDPn/Fd7dYjHoSfci/lYHQqVt8nMz/GhYpy73s/vbOlSGbil9PvRDVg0EZUQdxOkK0p4kspjKF5gHtSkSCp7Z3mUJmDnqAlBoDsIMhPeQSntgInAyO/JxBeAblTwLXqmR24ec0mTG6N4UjfYM7JYCKlcOfjW7ICTut2LThlrGOpALdA0msor1Nw5vbeOF01Nrdj6fwObHj/cE4dOLvzeWN73eYaOjF+3IGT+92Yjxi2UmUsNEob2M0rLSRT3OBweOUili+ZFbjUwd7eeCZ4CLq+/iK8n/myHsKJlAplWNne3ngoQ9TM31PWOpHlChxiWgTbvvvZvD7jlSZIr9nlZ03yLPGSz+s6cbq4UU1B8vUVVp8tqOpNOUVUg5xOkI0rm+W6MuTUrqBDNcMK/pwyUZqDHqe2OWWxioqUvKh6W5MWKNvn0vkdeHnFRXhv5eVYvmQW1m7szsom6TRfyuvkd+n8Dtx7zdmu+9Sa7fK2zs2ZbJZOnN5Xt+P8uoXTcjLb2QW0TnXgjCFl1vZeOLs9Z/u89A2dfD1jvzc31IVSfNxMiwru/PwZeHnFRdhVYObDmBb1zCb58oqLcN+yeQDSWeLm3fUs7npiCwYSqUzqbSdRETTXh5NRzkqLCi6c3Y5bH9scuOdrcmsMdz2xJVDgoEUFy5fMKmr5BiNbY7lNbo35ro9opkVOfmdGRTJzXK3ZbHvjibKcUGoRwT1XngWg+PO9S8EYIuxGkE7s4rfeaFuThvsKzFbbpEWw6Y5LsXrZPM/vCKug37vFYGTsreaADWDQRlRR/AQj5RBWu5xO1iMivlPfA84lDgBkTtT7BoehRbN//txKDBRSP83MejLjxkh9nU9AHkZ6dDO3shF25QYeenW35/ojIrbvp9vxdPfSMzMnGG4BrdsFgNs6N+PmNZuy2mvMFwp6wmEtrRD2iWFzfTQnm+AdV5yR14+zSLon7R+uOstxmVseeSMr4DZOuI/0JzL/d5NUqmhzAJvr67B+e0/g49pIlOQWfF2/cFrWe9/WpNlmcQzb+u09tse7FpGc76egif2vXzgN19tc5LAygtOjeQwBTaROjk5IKoW1G7szPWzW9ymFdK9XoSUK/IpIuidy1TM7cN3/+U3Je3wi4lwiphBu22EEYLsOxX1/Toyhw07fu9f7SJefSCnc1rkZq57ZEfiCysenjSnahR4n5jI1rTENAoWb1mzC9BVPYf53ns1rGHUlYMp/ogoTNFFHNbXLz9yHfIuCW+dzAemrUmOaNPT2J3yXGCiU3yE6hZZQCJK+v61JQ9ftl+a9rkKGHTm9n4W+B26lHI7GE7b7xlyw3e+wIutzC9kXEUlnV7Qej1adXd1Y/ugbOT16p01oxtsH+hxf35xOfvqKp/JqYznlOyfJOHF2CtrMnzW74+7bj71Z1KyeHfpQZmtmSCDY/FK71wW85xO1xjTc+fkzcPMjm/LqbbNyK6QtAO5bNq/ik8GEZXWJtzXfYdTGPFFzdldjDrFb5kezUiU8uX7hNKzf3uMro6wTI52/3XkB4Fx6o1z8pvxn0EZEJeWnTpA5U6Dfk/l5dz1rewWwNaZh0x35ByxB+QmmtIigpbHO8+Tdjd/gId8fJ/P7VOivRD7vp1dQ51TXyi3Nv7kuT9CadeaTAL8T/81zQMzZDb041r1zOVk2hBFclovfICQoYx6L3XsXAYpefsLQpEXQoEVdP/de80TNKjHxhl1ZlHK6fuE0PPnGvqIlmtm18nLM/86zRR1iWyg/gU+lJFeyS3JjfB6CBHB+vgeLXfM2CL9BG4dHElFJmedlpRzOQJNKZYa1mYemuXH6US51Vji3uXTGUA3oVzqDbqOZ21AX89DCfAM2P0M8/Q6CCvp+2g3HtD7PaThnr8vJk/m9CTof01jeul63OZJH4wl0tMawetk8dN1+qe/3wTHLn48zFuO5dsdHmIzhR2ERpNtcjHYbCXGchvQFVR/NHdroR38i5etzb1db0s7k1lhZa03ZzT0VQcUEbEA6yVJzQzrnXtiDNmNaBItWrsOR/kTOa5dmgKg/fgKdREohpkXK3u7e/qGsqRLG+cKulZfjuoXTfA+9NTIXuw1nr8Y5kMweSURl46emWT6lBcrJKTW/MURw0cp1OYFkPtvoVZagEH7my8W0aE4xYD9DbPxsq98so3aZMJ16KYygwOC3/p/dc83rdep5M3qQ86lpWEitvzExDYtWrssMhRocThZcaNrKqFmYb1ZOOwrZGTobtUimRyrosEEr4+QsjJO05voo6uu8i7b7YU6cE7Q3wTynuFwp383tNHqSb66gYZGC7H0Tdj9SPJHKHPvmGoNGBtS7nthS0T1wVvFEquTDPa2M+bLm700AgbPJGhdqxriU82BxbSKiAPzWNPNzstXmkM66GBPF3XgFU2Fl0DTWVUiQ5jQE0a0tArgO7Qrj/SxkHzkdUwrATWs24a4ntmQNU/SaUyQArls4zXcdQbshv0GD8nxr/WkRQd/QcOYkpTeegBYRxx7tIK/rNJzXrq3G8kYPhJ+1m2s99cYTiGlR3LdsXmY9C04ZG/jEzWCcnBUSDBvCLsZunJwGCSyiIpljavmSWbjqnA48/NsPco67iNiXwSiGAX1OYBj7OCxN9dGiJc2xY93VbgFb0LlaXvM2wxAVwdL5HQXVtQxTPJHErY+9CUACfxcavxVuHXPlTvCWDwZtRFQ2fk54AX9XxO644oyc5A1aVHDHFWeE12Cf3IIpp5OaUl/1cyt07tRGrzkAYb2fhewjcxvsXuNIfwLLH80tOG5YcMrYwL2X5teZ4ZAAJEhQbhdQOvViin7257SMMexpIJHKu6dh1Redh9g6XaQAgvUAOZVvMNcXzCdznblHqpDC98ViBGB+WOeLGcW3Idn1J42e/VL2epmDyFL01AjgWXeu32fAVujnw8raS2Qn6LoGEilMaWssatBmHEPLl8yyTd4BlPZCAJB/UW7jt8JtuHy1jN4xYyISIqoYTskl/GaTrNTMm2aFbmNYnIa1GUN7wmhjvtsa1j4qxyR0t/1ayPr87BOn5CpGRr98rqDn2+6whk2ae3ZvXrMp0MluVATXnjc1M4R3TEzD0HCyqJkig4hpUd8Bm1eSHSu/SV2Cvq4bI1lPKTKX+llXh89ePy0qodderEbGZ81v8im33kLjs2fXA1xs1ukIxfg+DhsTkRBR1XGrFeb3+UaSk3IWI3dT6DaGxW0IYlhtzPd1wlp/OSahF6vWop994lYHMZ9el0LaHdb+NSftaA041Pna86ZmFaDvjSdCC9gKTdrQ1qRl3k+/ywYJrPb2xj2TuhjH0B1XnBFKAgrj+CtFnTZjXU51F1tjmu+kNgzY0ozPmt+9oZDez3b72KjpZ1cXtZjamjRcdU66V96tXms1Do0EODySiCpMofO0qkElbKPXEMSw2pjv64Sxfrf5NcUajlrMBDFe++TC2e146NXdOSdd5qQoQeaYXXVOdsKVINsU9tymeCKJhrpITu+U0/a0xrS8CnX7YVzJB5yH4Zo51Ys0OCXE6bAsG6S24OTWWKBj8ZcbduPlnYd9vbYd84lwkJ4Vp7nIftd15+fPyBnKp0UkkygHCJ7EIix+SnRUu6PxBO5bNs/22Iwnkli/vQf3XHlmaOVj3GgRweVnTcqal2zM621z+fxVEwZtRFRTqmGIZCVwGgJZrVcg7TjNzdCiUtTtLEdQ3tnVjbUbuz1PioJcRTcysLnNf3TazqDzx4yiwW4ndsYJolfyGCO7pd/exVZTwWGvoNYaSPnJoBnV59ba7asggZXfYMh8fPs9Fncdcg88zUPnjP1uLRZurMfvsMR8agra7X/Aef8Z/9762Js586PCHBpqJ5+ALeowD7hSGRcHnD5rxsgNwN8FjkIkUsp2OGYipdBUX4eu20tXr7VYGLQRUc3I5+RypCpmj1ClsLvSHqTIdTXxU6bBzE+PW3dvHDNWPBU4I6Zx4SSeSGZOQr1O5M09I04n8cYJop/kMQAcE+GY2RXzderRcpoH4zUUNJFUrtlD/QZWfoOh5vq6UIcSA+ljxWsOUJBCyObA0m+ALw5tcNt/J38TsgM243sAQFlT3JsJgHuvObti2uPFfJHPKSnM5NaY76zCYXD6vFdjTTY7DNqIqGb4re9FaZUwTLPYRsI2AsFOSozeCj9XvhWCnQhZT9CSSmVO7pzW1xrTst4jv73A1l51o0SA0QavgM3uNTOlDGwy0Tr1zvoZChrGSaPf4OZonmUR3LYhprmnQLC+714XBJb9wdTMe2UN8J16m6xDmv2MqnC6mNFkCmzDDpKCJJgxm9way9Tty1cpszs26sdEZ1c3TgzkFoTXooILZ7e7DuvtCKEOo5nfY6daMREJEdWMMGugkb3Orm4sWrkOM1Y8hUUr16Gzq7vcTSI4n5RYU0IYgYqRtMdPIowg63S7cOKUpOXOz2eX5fCTdMUIEozECUavujkIsBKke1h8Jbexnvc5nAh3dnWjbzD3hNUqjJNGY784Jd/wuy67z7BX0o54IoXpLp/5oD2967f3ZL2HwMkA3y55hTXAdnv/zZy++41e5Hl3Peu7zVZNWiRTP81IvmIcV/no7o0XPHywlOn4j/QncOtjm3HXE1tsywPURQRrN3a7XjxZvmQW7l56pu+kPF78HDvVjEEbEdUMp5OVWrnKFkQxgiu/J0pUek4B0XULp7kGP/le0HA6EQorK6lXJli34NBtm7puv9Qzu+yqZ3bknIQmUiqnF8T4PHgluQjzpHHp/A5suuNSrF42zzZ481qX02cYgK8T56DBkZO9vXHH99BIXuF2nLi9/2Zu3/1GRtF8tTU3oOv2S7Fr5eW495qz0aHP+Vv1zA401xc3Y+L1+ue63OKJpOOcwHgi5RnIG+9XGBeRWmNaVgBYzuzMxcLhkURUM0ZCcg0/ijW3j8NPSyOfZDr5zlEMkuUxKoKUUq6vXaqspG7BYSHF2Tu7uh33h3WdTr1L5qQqbvuqkKRJxn4M+hpun2EjkPWqs2b3mQ+aLdRIamLHCPDN23fzmk2Z3tql8zt8j6ooZkF1Y11237fF7BHpaI3h7qXp3jyn2ozVIsz3SyT9XtTykHgGbURUM0ZCcg0/ihVclXP46UjJClpIwJ3PyYrdSZIWEUCy61f5LW5eqgsnboFZvm0w9r3bOs2cjvuj8QQ23eGeqc7ufb55zSbctGZTToZEN0Hfc6/PsN9e8+7eOBatXJdpp125CS2Snl9kHTlnzA90muNo7Ge3z4LfwNz6m5BPgOOUVMVYl933bbHKt1uP4zBKa2gRQUtjHY70J0LLXuk3M6fd+7Xh/cO2pUusmrRIVt1FY7im8Tq1iEEbEdWUWr7K5lexgqtCejDy1dnVnVNnqVaygtoFoqXuzXS60GF3n98gIt/nBuF0Rb5/KD2/zFwbym8b3OZlCXIDlUI+D3brMk5SgxzfYdXPm9waw22dm/HQq7s9224w2rnh/cM55SYEwLJzp2LBKWNds7e6Bdde8yP9Bubm34Qg5QUMCrmBm3ldQV/PKDHR25/wleHUrYe70J5EuwsEhfbedZi+Q9za5pRcaM3vPvC1/v5EbmgcTyRx5+Nbqvp3wQ2DNiKiGlOs4KrUw0/dUkWXclhmMXr5nHoRnE5witmb6XShI99tLMWFE6fCycbV9nuuPNMV3ZQtAAAgAElEQVQzRb2V2z62C6gK+Tx4vZ9+ju+w6ufFtKhjYXYv8UTStjaWUefv7qXOvbNeAb7bMNV8Lw449SxrUbENAszbY8xZM6+rs6vbd8F6wL7EhFfQlVIK7628POs+a3kFQ1uThsvPmoT123vQ3RvP9JzZBZ1Gz7kx/9nYNqf0/ennRXLKJ7htH4CsdhptcCq9YjefNKjeeCIzTLLWMGgjIqoxxQquSj381CsjXamGZZZyfmCtp6wO09L5HVj1zI6cZBL5BvR+h5rFE0nc8sgbuPeas/Pq0fO7Lq/jO59eWWP4mRFoRUVw1TkdWL+9J+/elUJqYzkF+G7BUEQk77lLbt9h+dToW/XMDsf9pkUlZ4ixU4kJt/Ib5s9++vsou1C4ef0DiRQWnDI2M+fNcFvn5pz33Fwew/z9pkXEtnSAFhXcc+VZrm213u/UozrgEPiF9Z1eq/OsGbQREdWYYgZXpRx+6vUDXopAptTzA4205yM9mY5fYQ4FDjLULKlU3j16ftfldXzns+2dXd1ZadiTSvmqkeU2R6kYFxrcgiFj3wP5XTjx6lkOcsHLbV+vuvps1+9gaw/+9Qun5bwX5nV3dnVj+S/fcO2JsvtucnrPjaL01vc+kVKZzKROw1qdatsZpQ/M7npii+/v0DDm6AG1W+aHQRsRUQ2qhbl9bj/gpQpkSj0/0Fz4utaTroRhTEyzTds+xqOWmR27ix39Q8MuKc3zD96tPSxuc6bsdHZ1O86HcguWgvbwAidP2AH7gObj08bg5Z2Hc5534ex2x3Z48Tt8FAj34lTQC15un2O372C7Hq61G7szvZ526/Y7dNBPllOv8hi98QRW68XqrZbO73AM2qzHUGdXt+Pnx27dy5fM8gxM/ajVkQmeQZuI/AjA5wAcUEp9zOZxAfDPAC4D0A/gBqXU62E3lIiIRhan3gin+RDFUI75gbUQcJeKzYV91/u9WPe915wjr+DCbT6keV1B5k0abbILsryCvSA9vALguoXTcoba2SXPsbN+e49jO7z46XGxzgO1G7qcb/kMv5+/fIeiu9Woc+q59XuhyG+WU7fyGACw/NE3ANj3Zna4BKtmTseGXTvN6zLPVdUigMs0Olu1OjLBT0/bgwD+FcBPHB7/LIDT9L/zAPxA/5eIiChvlVDCoVbmB9aqXoer+Nb7800mYyzjNNfJLXjv7OrG8kffyMxr6u6N46Y1m3DXE1tyLjoECRSc5npGRTzLMhTaw2vXzpsdel0K6Y32M3w0KuI67K5Y81ENxjFl7ql0KtdgPf78zgkz8xPI2n03eZXHuHnNJtuhqImkcuxJ9vu96HYMWLOxWjMFG0lV1m7sRiLlPztma0yr2e9Rz6BNKfWSiEx3WeQLAH6ilFIAXhWRVhGZpJTaF1IbiYhohCp3rxODq8rmpyfU7eQd8H5vg8x1Mp+cQwC7EYeF1pNyOhFOKZVX9sRCe3iL0RvtJ0GHVwKUYpbPsB5TRk+lcTyYszFeOLs9a66aW+BlNyfM4DV00Clg9HrPnYY6Atn1+6yfEz9JeLwCTXPZiDW/+yBr2470J/CzACUojO268/NnBHpONRHlo4ieHrQ96TA88kkAK5VSv9ZvvwDgb5VSG2yWvRHAjQAwbdq0c95///2CGk9ERFRp/PTq2A2781vAuhKVq/i5n/3oVJtLANTZZPhzeg+8ttFP+nYzp4yEXpy2x+/rhf1e+T2W812v0/Y6zcMz9oNTvTEBclLoB+XUppgWwUAilVOzLsgMrV0ubbPrjfIzVNy67y+c3Z6ZO+dWK84IBPP9rvL7mQijqHcph82HTUQ2KqUWeC0XRiISu8sCtnteKfUAgAcAYMGCBYWXXCciIqogfodklbqIdjEVexiaGz89oU49UwrICtgA9/fAqyfKq0SFVb7DB+1OogX+E3+E3XttNw+pUYtkLVPIMZJvplWnXp58ktT4bZNdDbMgJ7vWOWFWdvMgb16zKVNw3K3Ug/l51l5CO1pUMkNm8/2usn4+3TKCBiEAWps09PYnRtToh4j3Ip72AJhquj0FwN4QXpeIiKiquJ3gmBUrK2U5+N3mYlk6vwMvr7gI7628HC+vuMh2iFYQ+b4HQZ+X7/DBpfM7cNU5HVlXzBWAtRu70dnVnddrhmFw+GTAYgwBNdpTyDHitJ86WmO458oz0dEag5huG+//8iWzoEVy+xX6hoYL3k/FyE4YZK6sEXh164GQEQR7bZdRi87u4oK1SPeqq8/G0vkdBX9XmT+fTkGp27BQ6yMxLYr7ls1D1+2XOn7ma1UYQdvjAL4iaQsBHOV8NiIiGon8nuA4nfRVY6rqSg9Aly+ZhZgW9b18vu9BkOcVmszGrhh2KQNlK6+grJBjxO79M8/JcgrYl87vQEtj7oAyI8FGIYIeU17JTK0Bp5d8gmC3rKOGXSsvx66Vl6Pr9kszyUEiDgFVPp8Tp/fy2vOm2gbYWlRw3cJpjoH5SOMn5f/DAC4AMF5E9gC4A4AGAEqp/w/A00in+38H6ZT//6NYjSUiIqpkfpMyFCsrZTkUmoii2PPhvDJAmhXyHvgtzu0ny6OXQoKgYuxvr/YUcowUkgzIKbtoGBcUGuoimfe6rSk95NKuJpkA+MTMsba17LSoZHq0gsjn/fcavhsRQWdXd84wynxKS1iZj7kxMQ2NWsR2aONDr+7OXIxoro/i7/9o5AZodvxkj7zW43EF4C9CaxEREVGV8huM1VJWykIC0FLNh3PKAKlFBC2NdaHMjbG+p2NiGvqGhn0nOvHDOPl1Cj2dgiDjedZC3mHtb6+grNCLFJWU2dIuucZAIoWrzunIyhIJnKx151SzbjjPItL5bJdXoJpUKutYKKS0hJl1f/XGE5khjuYAce3G7qzjun8oiQ3vH67K78RiCSMRCRERESFYMFbucgZhKSQALWVCllIEynbFucNan1cmPqcgyPo8p2GVhewHr6CsXBcpitGj7VYY2ykN/owVT9m+llLIK2jOZ7v81HkzHwuFlJYw8/MZt1tGId3ztuCUsTXxPRkGBm1EREQhqpVgLIh8t7nU8+FK/d6EuT634W1ONbq8nmcodH/7CcrK8bkoRrDodszabaMxL8xpaG4+QXOQ7XLqZXWS75BWpwsUTvvLKK7tllVS6ds40r5PnTBoIyIiorIoxvC1WuV08iuAa302PwFZGPu7Ui9WhN2uIMesn+QfQH5Bs5/tsutlNQI3p9po+QxpdRvm7LS/BO6Fxg2VktCoEoSRPZKIiIgoMLfMgJQt34yjXo9zfwcT5Jj1W7uvWBcpnIYddrTGcO81Z7tux9L5Ha4lFbzWY/Qg2u2vIAXHeQHnJPa0ERERUVnUUkKWYst3fpZTMW7j5L1Y+7vYWUHLJcgx66eXKEjSHvM6L5zdjvXbe1zb4DWU02s7/PZSBl2Pnx42gBcUrEQFrEIelgULFqgNGzaUZd1EREQ0ctRKAJHvdpR6++2SphSaObMaLVq5zjZAiYogpZTv98IrCQ1gv3+d1t/RGnMdUhuU03paYxqaG+pyjrugy9c6EdmolFrguRyDNiIiIqpVDCBKr1TBQqUL69hz2p9W1v1bqmPfbj1aRACBbckLILf8xkj+TPoN2jinjYiIiGqW23wbKo5SZwWtVEHmhbnxu9+sy4W1fi9262lprMsK2IDsTJmlaFet4Zw2IiIiqlkjNYAo55BQp3lLY2JaSdZfScLIXul3Hphd0o5SZfW0rsepNp3xuavUbKOVjD1tREREVLPyzbpY6Tq7urFo5TrMWPEUFq1ch86u7qzHbn1sM7r1GlhGCnbzMsW0fMms9PA4i76h4Zw2uG0HpdllYLSqtKQdtfq5KycGbURERFSzarGsgFdQVu4hoUvnd6ClMXcwVyKpstpQ7uCyEvgJWu2GE16/cFpJhhfmG1TX4ueu3Dg8koiIiGpWLZYVcAvKls7vqIghob39Cc82eG1HrXMrSm3d/nIMJwzSPqta/NyVG4M2IiIiqmm1Nn/GKyhzmgNVyqFpftpQCcFlOZU7aPWa91ho+2rtc1duHB5JREREVEW85gtVwtA0P20Y6fOeyhm0+hmaWu6g2mlo5kidB8mgjYiIiKiKeAVElZBS3U8bKiG4LKdyBq1+5j2Ws31OQeVtnZtH7DxIDo8kIiIiqiJ+5gtVwtA0rzaM9HlPy5fMsi0yXYqg1U8vWjnb5xRUPvzbD5BUzvXfahmDNiIiIqIqUwlBWRhqZTvyUc6g1c+cw3K2zymotAZsXsvXEgZtRERERERlUK6g1W8vWrna5xRURkVsA7eRMA+Sc9qIiIiIiEaQSpj36MZpvuO1500dsfMg2dNGRERERDTCVPLQVLehmQtOGTsi50GKchgbWmwLFixQGzZsKMu6iYiIiIiIyk1ENiqlFngtx+GRREREREREFYxBGxERERERUQVj0EZERERERFTBGLQRERERERFVMGaPJCIiIiKiitbZ1T0is0YaGLQREREREVHF6uzqzioG3t0bx62PbQaAERO4+RoeKSKfEZEdIvKOiKyweXyaiKwXkS4ReVNELgu/qURERERENNKsemZHJmAzxBNJrHpmR5laVHqeQZuIRAF8H8BnAcwFcK2IzLUsdhuAR5RS8wF8CcD9YTeUiIiIiIhGnr298UD31yI/PW3nAnhHKfWuUmoIwC8AfMGyjAIwWv//GAB7w2siERERERGNVJNbY4Hur0V+grYOAB+Ybu/R7zO7E8D1IrIHwNMA/tLuhUTkRhHZICIbenp68mguERERERGNJMuXzEJMi2bdF9OiWL5kVplaVHp+gjaxuU9Zbl8L4EGl1BQAlwH4qYjkvLZS6gGl1AKl1IL29vbgrSUiIiIiohFl6fwO3HPlmehojUEAdLTGcM+VZ46YJCSAv+yRewBMNd2egtzhj18H8BkAUEr9RkQaAYwHcCCMRhIRERER0ci1dH7HiArSrPz0tL0G4DQRmSEi9UgnGnncssxuABcDgIjMAdAIgOMfiYiIiIiICuQZtCmlhgF8E8AzALYhnSVyi4h8R0Q+ry92C4D/KSJvAHgYwA1KKesQSiIiIiIiIgrIV3FtpdTTSCcYMd93u+n/WwEsCrdpRERERERE5Ku4NhEREREREZUHgzYiIiIiIqIKxqCNiIiIiIiogjFoIyIiIiIiqmAM2oiIiIiIiCqYlCszv4j0AHi/LCt3Nx7AwXI3gkYMHm9UKjzWqFR4rFEp8XijUinWsXaKUqrda6GyBW2VSkQ2KKUWlLsdNDLweKNS4bFGpcJjjUqJxxuVSrmPNQ6PJCIiIiIiqmAM2oiIiIiIiCoYg7ZcD5S7ATSi8HijUuGxRqXCY41KiccblUpZjzXOaSMiIiIiIqpg7GkjIiIiIiKqYAzaiIiIiIiIKhiDNhMR+YyI7BCRd0RkRbnbQ9VHRH4kIgdE5Pem+8aKyHMi8rb+b5t+v4jIv+jH25si8nHTc76qL/+2iHy1HNtClU1EporIehHZJiJbRORb+v083ihUItIoIr8TkTf0Y+0u/f4ZIvJb/bhZIyL1+v0N+u139Menm17rVv3+HSKypDxbRJVORKIi0iUiT+q3eaxRUYjILhHZLCKbRGSDfl9F/o4yaNOJSBTA9wF8FsBcANeKyNzytoqq0IMAPmO5bwWAF5RSpwF4Qb8NpI+10/S/GwH8AEh/WQC4A8B5AM4FcIfxhUFkMgzgFqXUHAALAfyF/p3F443CNgjgIqXU2QDmAfiMiCwE8A8A7tOPtSMAvq4v/3UAR5RSHwVwn74c9OPzSwDOQPp78n79t5fI6lsAtplu81ijYrpQKTXPVIOtIn9HGbSddC6Ad5RS7yqlhgD8AsAXytwmqjJKqZcAHLbc/QUAP9b//2MAS033/0SlvQqgVUQmAVgC4Dml1GGl1BEAzyE3EKQRTim1Tyn1uv7/40if4HSAxxuFTD9mTug3Nf1PAbgIwKP6/dZjzTgGHwVwsYiIfv8vlFKDSqn3ALyD9G8vUYaITAFwOYAf6rcFPNaotCryd5RB20kdAD4w3d6j30dUqIlKqX1A+kQbwAT9fqdjjsciBaIPCZoP4Lfg8UZFoA9X2wTgANInJDsB9CqlhvVFzMdN5pjSHz8KYBx4rJE/qwH8DYCUfnsceKxR8SgAz4rIRhG5Ub+vIn9H68J+wSomNvexHgIVk9Mxx2ORfBORFgBrAdyklDqWvshsv6jNfTzeyBelVBLAPBFpBfAfAObYLab/y2ON8iIinwNwQCm1UUQuMO62WZTHGoVlkVJqr4hMAPCciGx3Wbasxxt72k7aA2Cq6fYUAHvL1BaqLfv17nPo/x7Q73c65ngski8ioiEdsD2klHpMv5vHGxWNUqoXwItIz6NsFRHj4q/5uMkcU/rjY5AeNs5jjbwsAvB5EdmF9DSVi5DueeOxRkWhlNqr/3sA6QtS56JCf0cZtJ30GoDT9AxF9UhPYH28zG2i2vA4ACOT0FcB/F/T/V/RsxEtBHBU74Z/BsClItKmT2S9VL+PKEOft/HvALYppf7J9BCPNwqViLTrPWwQkRiAxUjPoVwP4Gp9MeuxZhyDVwNYp5RS+v1f0jP+zUB6Mv/vSrMVVA2UUrcqpaYopaYjfR62Til1HXisURGISLOIjDL+j/Tv3+9Rob+jHB6pU0oNi8g3kd7JUQA/UkptKXOzqMqIyMMALgAwXkT2IJ1NaCWAR0Tk6wB2A/iivvjTAC5DeoJ0P4D/AQBKqcMi8l2kLyQAwHeUUtbkJkSLAHwZwGZ9rhEAfBs83ih8kwD8WM++FwHwiFLqSRHZCuAXInI3gC6kLyJA//enIvIO0r0eXwIApdQWEXkEwFaks5/+hT7sksjL34LHGoVvIoD/0KcV1AH4uVLqv0TkNVTg76ikL0gQERERERFRJeLwSCIiIiIiogrGoI2IiIiIiKiCMWgjIiIiIiKqYAzaiIiIiIiIKhiDNiIiIiIiogrGoI2IiKqGiJzQ/50uIn8c8mt/23L7lTBfn4iIKF8M2oiIqBpNBxAoaNPrjLnJCtqUUp8I2CYiIqKiYNBGRETVaCWAT4rIJhG5WUSiIrJKRF4TkTdF5E8BQEQuEJH1IvJzAJv1+zpFZKOIbBGRG/X7VgKI6a/3kH6f0asn+mv/XkQ2i8gy02u/KCKPish2EXlI9CqtREREYaordwOIiIjysALAXyulPgcAevB1VCn1ByLSAOBlEXlWX/ZcAB9TSr2n3/6aUuqwiMQAvCYia5VSK0Tkm0qpeTbruhLAPABnAxivP+cl/bH5AM4AsBfAywAWAfh1+JtLREQjGXvaiIioFlwK4CsisgnAbwGMA3Ca/tjvTAEbAPwvEXkDwKsAppqWc/KHAB5WSiWVUvsB/DeAPzC99h6lVArAJqSHbRIREYWKPW1ERFQLBMBfKqWeybpT5AIAfZbbiwGcr5TqF5EXATT6eG0ng6b/J8HfVSIiKgL2tBERUTU6DmCU6fYzAP5MRDQAEJHTRaTZ5nljABzRA7bZABaaHksYz7d4CcAyfd5cO4BPAfhdKFtBRETkA68IEhFRNXoTwLA+zPFBAP+M9NDE1/VkID0Alto8778AfENE3gSwA+khkoYHALwpIq8rpa4z3f8fAM4H8AYABeBvlFIf6kEfERFR0YlSqtxtICIiIiIiIgccHklERERERFTBGLQRERERERFVMAZtREREREREFYxBGxERERERUQVj0EZERERERFTBGLQRERERERFVMAZtREREREREFYxBGxERERERUQVj0EZERERERFTBGLQRERERERFVMAZtREREREREFYxBGxERERERUQVj0EZERERERFTBGLQRERERERFVMAZtRERUkUTkRRE5IiIN5W4LERFROTFoIyKiiiMi0wF8EoAC8PkSrreuVOsiIiLyi0EbERFVoq8AeBXAgwC+atwpIjERuVdE3heRoyLyaxGJ6Y/9oYi8IiK9IvKBiNyg3/+iiPyJ6TVuEJFfm24rEfkLEXkbwNv6ff+sv8YxEdkoIp80LR8VkW+LyE4ROa4/PlVEvi8i95o3QkSeEJGbirGDiIho5GDQRkRElegrAB7S/5aIyET9/n8EcA6ATwAYC+BvAKREZBqA/wTwvwG0A5gHYFOA9S0FcB6Aufrt1/TXGAvg5wB+KSKN+mN/BeBaAJcBGA3gawD6AfwYwLUiEgEAERkP4GIADwfZcCIiIisGbUREVFFE5A8BnALgEaXURgA7AfyxHgx9DcC3lFLdSqmkUuoVpdQggOsAPK+UelgplVBKHVJKBQna7lFKHVZKxQFAKfUz/TWGlVL3AmgAMEtf9k8A3KaU2qHS3tCX/R2Ao0gHagDwJQAvKqX2F7hLiIhohGPQRkRElearAJ5VSh3Ub/9cv288gEakgzirqQ73+/WB+YaI3CIi2/QhmL0Axujr91rXjwFcr///egA/LaBNREREAABOuCYiooqhz0+7BkBURD7U724A0ApgEoABADMBvGF56gcAznV42T4ATabbH7FZRpna8EkAf4t0j9kWpVRKRI4AENO6ZgL4vc3r/AzA70XkbABzAHQ6tImIiMg39rQREVElWQogifTcsnn63xwAv0J6ntuPAPyTiEzWE4Kcr5cEeAjAYhG5RkTqRGSciMzTX3MTgCtFpElEPgrg6x5tGAVgGEAPgDoRuR3puWuGHwL4roicJmlnicg4AFBK7UF6PtxPAaw1hlsSEREVgkEbERFVkq8C+P+VUruVUh8afwD+Fel5aysAbEY6MDoM4B8ARJRSu5FODHKLfv8mAGfrr3kfgCEA+5EevviQRxueQTqpyVsA3ke6d888fPKfADwC4FkAxwD8O4CY6fEfAzgTHBpJREQhEaWU91JERETki4h8CulhktOVUqlyt4eIiKofe9qIiIhCIiIagG8B+CEDNiIiCguDNiIiohCIyBwAvUgnTFld5uYQEVEN4fBIIiIiIiKiCsaeNiIiIiIiogpWtjpt48ePV9OnTy/X6omIiIiIiMpq48aNB5VS7V7LlS1omz59OjZs2FCu1RMREREREZWViLzvZzkOjyQiIiIiIqpgDNqIiIiIiIgqGIM2IiIiIiKiCsagjYiIiIiIqIIxaCMiIiIiIqpgvoI2EfmMiOwQkXdEZIXDMteIyFYR2SIiPw+3mURERERERCOTZ8p/EYkC+D6ASwDsAfCaiDyulNpqWuY0ALcCWKSUOiIiE4rVYCIiIiIiopHET0/buQDeUUq9q5QaAvALAF+wLPM/AXxfKXUEAJRSB8JtJhERERER0cjkJ2jrAPCB6fYe/T6z0wGcLiIvi8irIvIZuxcSkRtFZIOIbOjp6cmvxURERERERCOI5/BIAGJzn7J5ndMAXABgCoBficjHlFK9WU9S6gEADwDAggULrK9BREREREQUms6ubqx6Zgf29sYxuTWG5UtmYel8a/9T5fMTtO0BMNV0ewqAvTbLvKqUSgB4T0R2IB3EvRZKK4mIiIiIiALo7OrGrY9tRjyRBAB098Zx62ObAaDqAjc/wyNfA3CaiMwQkXoAXwLwuGWZTgAXAoCIjEd6uOS7YTaUiIiIiIjIj1RK4e+f2pYJ2AzxRBKrntlRplblz7OnTSk1LCLfBPAMgCiAHymltojIdwBsUEo9rj92qYhsBZAEsFwpdaiYDSciIiIiIjIMJJJ4ZedBPLf1AF7Yth89JwZtl9vbGy9xywrnZ3gklFJPA3jact/tpv8rAH+l/xERERERERXdoRODWLf9AJ7fth8vvXUQ8UQSzfVRXDBrAl7ZeRBH+hM5z5ncGitDSwvjK2gjIiIiIiIqN6UUdvb04flt+/H81v3YuPsIlAImjWnE1edMwSVzJ+K8U8eioS6aM6cNAGJaFMuXzCrjFuSHQRsREREREVWs4WQKr+/uxXNbP8Tz2w7gvYN9AICPdYzGty4+DYvnTMQZk0dDJDvpvZFsZKRkjyQiIiIiIiqZE4PD+NVbPXhu236s334AR/oT0KKC82eOx9cWTcfFcyb6Gua4dH5HVQZpVgzaiIiIiIio7D48OoDnt+3Hc1v34zc7D2EomUJrk4aLZk3A4rkT8cnTxmNUo1buZpYFgzYiIiIiIio5pRS27juG57emE4ls7j4KADhlXBO+cv4puGTuRJxzShvqon6qlNU2Bm1ERERERFQSQ8Mp/Pa9Q3huazqRyN6jAxABPj6tDX/7mdm4ZO4EzGxvyZmfNtIxaCMiIiIioqLp7R/CizvS89P+e0cPTgwOo1GL4JOnteOmxafjojkTML6lodzNrGgM2oiIiIiIKFS7D/XjuW378dzWD/HariNIphTaRzXgirMnYfGciVj00fFo1KLlbmbVYNBGREREREQFSaUUNu3pxfNb9+P5bfvx1v4TAIBZE0fhG58+FZfM/QjO6hiDSITDHvPBoI2IiIiIiAKLDyXx8jsH8dzW/Xhh+wEcPDGIaERw3oyx+NIfTMPiORMxbVxTuZtZExi0ERERERGRLz3HB7Fu+348t/UAfv1ODwYSKYxqqMOnZ7XjkrkTccHpEzCmaWSm5S8mBm1ERERERGRLKYV3DpzQ56ftx6YPeqEU0NEay/SmnTtjLOrrmJa/mBi0ERERERFRxnAyhdd2HcHz29Lz094/1A8AOGvKGNy8+HQsnjMRcyaNYlr+EmLQRkREREQ0wh0fSOCltw7i+W37sW77ARyNJ1BfF8GimeNw46dOxcWzJ+IjYxrL3cwRi0EbEREREdEI1N0bxwv6sMdX3z2ERFKhrUnD4jkTccncCfjkae1obmC4UAn4LhARERERjQBKKWzZewzPbU0Halv3HQMAnDq+GV9bNAOL507Ex6e1Icq0/BWHQRsRERERUY0aHE7iNzsPpeenbT2AD48NICLAOae04dbPzsbiuRMxs72l3M0kDwzaiIiIiKhsOru6seqZHdjbG8fk1hiWL/3IUWYAACAASURBVJmFpfM7yt2sqnakbwjrdxzA89v247939KBvKImm+ig+dVo7Fs+diAtntWNcS0O5m0kBMGgjIiIiorLo7OrGrY9tRjyRBJCeY3XrY5sBgIGbA6cg972DfXh+6348t20/Nuw6jJQCJoxqwBfmd+CSORNx/sxxaNSi5W4+5UmUUmVZ8YIFC9SGDRvKsm4iIiIiKr6h4RR6+4dwuH8Ih/uGcKQvgcP9QzjSl7695rUPMgGbmRYVnDWlFVERRCOCuqggIoK6SPq29a8u6/+R9LIOz6mz/D9i/Ks/JxqJnFyv07I2j2WeF5Ws59stm2+qfGuQCwB1EcHYZg0Hjg8BAOZMGo1L5kzA4rkT8bHJYxDh/LSKJiIblVILvJZjTxsREREReRpOpnA0nsCR/iEc7kukg7BMMDZ0MhjrT+CIft/xwWHH1xvVWGcbsAFAIqkQ06IYTqWQTCkMDieRTCkklcJwUiGlFIZTKn2f/jecUkhZ/jWek0yVp5PCSURwMpDMBKaRnCDTGvS9tf84EsnsbRlOKRyND+POK+Zi8dyJmNLWVKatomJi0EZEREQ0wqRSCscGzIFXOhjLCr4s9x2NJ+A0QKupPoq2pnqMba5HW3M9ZoxrQltzPcY2pW+Pba43Pa6hNVafrgG2ch26e+M5r9fRGsPP/uS80LZXqewAzi7QM99nXib9nBSGkyef7xggml9DKSSTKSQV0s93W9a8rqT+upnANJV5fMveY7bbNzScwg2LZoS2v6jyMGgjIiIiqmJKKZwYHM4ZepjpBbMZmnikfwhOnU/10Ugm+BrbrGHu5NGWoMsIxrTM/fnOlVq+ZFbOcL+YFsXyJbPyej0nog99rPYTX6cgd3JrrAytoVKq9mOXiIiIqKIUmg0xPpS0D74yvWCJnKDMOmTOEI2IHmxpaGuqx2kTWiw9YNrJYEz/t6k+mvecq6CM/cLskf6UKsilysNEJEREREQhsUsU0VgXwV9efBrmT23NGXpoF5QNJFK2ry0CtMa07KDLLvgy3T+6sa5kARiVBksk1Ba/iUgYtBERERHZGBpO4cTgMI4PJHB8YFj/S+j3DePE4DCODSRwYuDk7V+/fRBDSfugy2p0Y11OkHWyxys3CBsd0xBlJkCimsLskURERDQipVIKJ4aGTcFUAscGTt42B152gZgRpA0Oewdf9XURjGqow6jGOrQ01rkGbL+4cWEmKGtt0qBFI2FuNhHVMAZtRERE5KpUw7GUUhgcTuX0Xpl7uozb6V4u/T79cXPg5UUEaGmow+hGLR1wNdRhfEs9po9vxqjGupOBWEMdRjVqaGlM3x7dqOn3pYO0hrrsBBxu2RAXnjoutH1FRCMLgzYiIiJyZJ2j1d0bx62PbQaArMBtOJly7L06bgRaDoHY8cH0YycGhx0TapjFtGgmiBqlB1UTRjWmb+sB1mibgGuU6XZzkZJtMFEEERUDgzYiIiKyNZxM4XtPb8spgBxPJLH80TfwL+vezgRiTkWSzaIR0QOrOoxqSAdPk1sb0dLQYh9c6T1ao/TesFGNdWhuqKvoYYXMhkhExcCgjYiIiHBsIIFte49h275j2LbvOLbuO4a39h93nNeVSCrM+cjoTDDV0qBlhgyOttw2grRGLTIiMhkund/BII2IQsWgjYiIaARJpRT2HIlj676j2LrvuB6kHcOeIyfnYY1trsecSaPw5YWnYO3re3CkP5HzOh2tMXz/uo+XsulERCMWgzYiIqIaFR9KYsf+dGC2Ve9F2/7h8UyijogAM8Y3Y97UVlx77jTMnTQacyePxoRRDZkesY91jOEcLSKiMmPQRkREVOWUUth/bDAdnOk9Z1v3HcOug31I6Xk9WhrqMPsjo3DlxzswZ9JozJk0GrMmjkKsPur62pyjRURUfgzaiIiIqsjQcAo7e05kes62fZieg3a4byizzJS2GOZMGo0rzpqMOZNGY+6k0ZjSFkMkz8LMnKNFRFReDNqIiGpAqepoUWkd6RvK9Jpt1ROEvHPgeCYtfkNdBLM+MgqXzJmIOZNGYe7kMZg9aRRGN2plbjkREYWJQRsRUZXzW0eLKlcypbDrUF8mKci2fcexde8xfHhsILNM+6gGzJ00Gp8+vT0doE0ajRnjm1FXwenviYgoHAzaiIiqmFsdre89vQ2LPjoerU1aRde1GmlODA5jx4fpxCBG9sYdHx7PvIfRiOCj7S1YeOpYzJ08OjP/bHxLQ5lbTkRE5cKgjYioChwbSODdnj7sPHACO3uMvz68f6gvM1TO6sDxQfzB3z8PABjdWIexzfVoa67H2Cb93+Z6tDXVY2yzpv978vExMS3v+U+UppRCd28c2/aZsjd+eAzvH+rPLDMmpmHOpFH40rlTM3PPTpvYgoY69+QgREQ0sjBoIyKqEKmUwr5jA9mB2YE+7Ow5gQPHBzPL1UUEp4xrwsz2FlwydyIe/t1u9NrU0Wpr0nDzJafjcN8QjvQN4XB/Akf6hrDv6AC27juGQ31DGHIonBwRoLWpHm1Nmim4swZ92cHeqIa6EVE42c5AIom395/Iyt64bd8xHBtIp9YXAU4Z24QzJo/G1R+fku49mzwak8c0jth9RkRE/jFoIyIqsYFEEu8d7MsKynb2nMC7PX1ZwxxHN9Zh5oQWfOr0dsxsb8HM9mbMnNCCaWObsoY7zpo4yraO1h1XnOE6p00phXgiqQd1CRzu14O7viEc6df/+hI43DeE3Yf7semDXhzpH3Ls2auLiCmoswR7OUFf+vGYFq26oKXn+GBOcLazpw9JPbd+U30Usz4yCp87ezLm6kMbZ39kFJob+JNLRET54S8IEVERKKVwqG9I7zXrMw1pPIE9R+JQetwjkk7PPrO9BQtPHZcVnI1rrvcV0ORbR0tE0FRfh6b6Okxp879dJwaHbYO8rH/7Enhr/wkc0e9L2cd5aKiL2AR1mmX45sl/W5s0NGqFDx30k21zOJnCuwf7TAFaOjnIwRMnez0nj2nEnEmjcencj6SHN04ejVPGNnFoKRERhUqUcvglLbIFCxaoDRs2lGXdRERhSSRT+OBw/8nA7MDJ+WZH4yeHLMa0KE5tb9aDshbMnJD+/4zxzaEEIZUslVI4NpAwBXUJfbimXdCXXs6876ya66OZoK61yRTk5czVS/fotTXVZ/VMWrNtAkCjFsGf/OEMjGtpyGRv3LH/eGb4aH00go9OaDElBklnb2xtqi/ejqskbz4CvPAd4OgeYMwU4OLbgbOuKXeriIiqnohsVEot8FyOQRsRkbdjA4nsXrMDJ/DuwdxEIBNGNWQFZen/t2DS6Eb2vgQwnEyhN56wBHUJU0+eKejTh3GeGBx2fL1RRiKWpnps23cMgw5z+QBgXHP9ycBMD9JmtreM3Aycbz4CPPG/gET85H1aDLjiXxi4OWGQS0Q++Q3aODySiEiXSinsPRpPB2aWLI09lkQg08c3Y2Z7My6dOzETmJ3a3syixiGpi0YwvqUhUJr7weEkevWeuuyevETW8E23gO13374Y7aMaqm6eXVG98J3sgA1I3356OTBwFJCI/iem/+t/EIfHnJb1sZyI6XXdlnVazrqsWNrqspwf1iD36Afp2wADNyLKm6+gTUQ+A+CfAUQB/FAptdLy+A0AVgHo1u/6V6XUD0NsJxFRaAYSyXT6fFNQlu45O4GBxMkT+tGNdfjohBZccHo7Zk5oycw3m2pJBEKVoaEuiomjo5g4utF1uUUr16G7N55zf0drDBM8njtinDgAvPcS8O6L6aDDzkAv8PRfl7RZZZcTjFoDPAEGjwPKcmEgEQeevBk48j7Q0g60TARaJgDNE9L/1rEGHxG58wzaRCQK4PsALgGwB8BrIvK4UmqrZdE1SqlvFqGNRESBKaVw8MRQTur8nT0n0N2bnQhkalsTTm1vxvkz80sEQtVl+ZJZttk2ly+ZVcZWldnAMeD9V4D3/jsdqB3Qf+IbxwB1MWA4N8jF6MnAjS+lAxSVAqBO/j/zp/Q/y/05yzosl7Ossvxrt6zda9stC4f22tx23Dab///u3+z38dAJYP3d9o81jtEDuYlAsymoa5mQfV9zOxDlICmikcjPJ/9cAO8opd4FABH5BYAvALAGbTXhggsuyLnvmmuuwZ//+Z+jv78fl112Wc7jN9xwA2644QYcPHgQV199dc7jf/Znf4Zly5bhgw8+wJe//OWcx2+55RZcccUV2LFjB/70T/805/HbbrsNixcvxqZNm3DTTTflPP69730Pn/jEJ/DKK6/g29/+ds7jq1evxrx58/D888/j7rtzfzD+7d/+DbNmzcITTzyBe++9N+fxn/70p5g6dSrWrFmDH/zgBzmPP/rooxg/fjwefPBBPPjggzmPP/3002hqasL999+PRx55JOfxF198EQDwj//4j3jyySezHovFYvjP//xPAMB3v/tdvPDCC1mPjxs3DmvXrgUA3HrrrfjNb36T9fiUKVPws5/9DABw0003YdOmTVmPn3766XjggQcAADfeeCPeeuutrMfnzZuH1atXAwCuv/567NmzJ+vx888/H/fccw8A4KqrrsKhQ4eyHr/44ovxd3/3dwCAz372s4jHs098Pve5z+Gv/zp9pZrHXvaxd/DEIBoXfwuHMAr1u1+F9tbzOUPlHn30UYxpG4v77n8AD/3kJ4gnkpm/gaEkxl51ByJaI46//hTiO36NWH0UMS3911gfxS+f+C9MH9eMf/3n+/Dkj5/ELgDGEcZjr7aPvW9+6zv4+Y5hvLPhRQy8/jhaxsaw+lcNWK0/XvPfe/f/b2DPa7jxmzfjrbffAQZPAFCARDDv9KlYfdsdwKmfxvV/80/Y89Ym4ODbmUDn/ClR3PPZNmDxXbjqq9/gsWexesF4zGs5iOffHcbdL50cVo26BqDjHPzbvd/FrEkteOLxJ3DvD38BJBNAcghIfQAkd+Kn107GVK0Xa14/jB9sGMp5/Ue/2oHxEyfjwa4BPPjKh0C0Xv/TgGg9nn74/6BpwnTc/+NH8Mgvf5nz/LIfe/zeq8jfXKCGv/f6enB63T48sCRV1XNM/QRtHQDMYyP2ADjPZrmrRORTAN4CcLNSKmc8hYjcCOBGAJg2bVrw1hLRiHDwxCDe7enD2GMDqBs9Cof7htDX04eBRAoNdZF0UJZI4o/ufxn7BjT0vrEDJ/YfBwDU10UQ06IY19KAb182B3OnteNX49/Gs0O515lmf2R0qTeNKsTiuRPxF380C0880Yd7D75U7uaUxtAJIN4LvL0LWHlKuvds3wCApvSJTGwM0DAamPNx4JN/lX6OSLp3BwCO7AKGB9PLZJKQrCnPtlSyc/8UePufABw/eZ9EgLbp6X9HfQTomAVM2QuMejH3+V/7KTB1KvDQT4AP7z8Z1CWH0v8//VIgehwY7koPxUwOZQ/H/PeLAU2A1xLpszc9mMv8+5v70z14h98FEv3p+yLsvaMa1deTvug0FgBiVT3H1DN7pIh8EcASpdSf6Le/DOBcpdRfmpYZB+CEUmpQRL4B4Bql1EVur8vskUQj23AyheMDwzg2kMDReALH4if/f8/T23BswDkToBYVTB/XnJOl8dT2ZoxiIhCi9HC9w+8C764H3v1vYNevgPiR9GPts4EZnwZO/TRwyqL/196dx9dV1/kff32zNem+71u6UCh0g1IKKBZQNmURN3B3QBRBHceNmfmN46Az4jAzOggKqCjjKFARkNWOQqEg0AXoQltKV9q0he7pljTb9/fHuW2TNmmTNsnJ8no+Hvdx7/3ek3M/yeNA7/t+v+dzoKB7urW2Rc3ZPTLGJLzt2Qy730nOR9y9KXm8p9rj3ZnXq2q5nEZWbu3LMWuMZR536FL/pixqGm2lO2mMUFWZmWkuz3xBsX/mueLgFxWV5dVer+u1MqisqPm4sgxm/yz57+NQ3YbA115v/t+5Fo3ZPbIIGFLt+WBgQ/UNYozV56d/DvywPkVKar1ijOwpq8wEruRWXFLOztKKao+TMHbwceZWWnHE9uxHMvMb0xjSo4AcG4FINe16Owloq59L7ndmlpZ1HQxjLkmCWuE50HVAunW2B+M/2nwfokOA/K7JrdfII28bY9JA5kCQqx7wMqFu5wbYMD95HisP30dOQbVmKtUDXi1jeR3r9zu0lRDSHOrqTlpRBmMvPRhWDgtBtYWieoSgQ3/2SK8dy37TUlx09G1amPqEtrnA6BBCIUl3yKuAj1ffIIQwIMa4MfP0MmBpo1YpqUmUllceMVgdMXyVVlBZdeSZ+i75OXTNz6VrQS7dCnIY2rNj5nFuZjyn2uPMeEEOV/70RTYWlx62v0HdCyjs3amp/hytmx962p/SYljzwsGgtvmNZLygBwx/N7z7azDiXOg5wpkRJUJIjo+CHtDnKI13qqqgZFsm3FWbqas+e7dtNax9GfZuBWr59yCvy8Ew16laqKveQXPdHHj6X+p3iYSqqiRIVlUkMzT77w+M7R+vY6yq4pCfP8LYgfG6xioO+fkj1XWUWmvUUHXIex2yXcm22ruTPnpDcmsKWfuX2OZkltPmZpbb5tZ8vH+pbW7HQ17P/GxW9aW6uQ3f72E/e6TX8iArG348rvYOuN0GN83fqgkdNbTFGCtCCDcCM0ha/t8TY1wcQrgZmBdjfBT4SgjhMqAC2AZ8tglrllqdR15bz60zlrFhRwkDuxfwzQvHcMWkQce938qqyK5alhfuzISr2scrDjw+0vWqAPJzs2oEqt6d8xjZpxNdM0Frf8g6+PjgeOf8HLKP8WLS377oRLv7NYTXhWofykth3eyDM2kbXk0+vOUUwLAzYcLVMGIa9B8PWc5E6zhlZUGn3smt38lH3rayAvZuqRnuqs/e7d4Em5cll5Eo3XH09y4vgYeuSy6TUD301BYM0xSyk2CQlZPcQtbBx1mZ10L24WNZOdXGsyEn7/Cx2rbLyoZ599RdzwX/WncIOhBuco/wWib4HPq4NX/pc/53av77CJBbkIy3Mkc9p62peE6b2otHXltfawD5wZXjuHziQPbuX2JYWk7x3qPPcBWXlCfngpWUs+soSwyzswJd83MOC1RdC3IOPD8485Vz2CxYh5zspv7z1Kmpgm6b9KOTa1/q0YLW7OsYVFXCxvkHZ9LWvgwVpcmHuEGnJQFtxHtg8Ole50utR8W+amFuM9z3sbq3PfPGYw9Ch43lJEG0rrED49X3UcfY/oCWRpj50Sl1zBz5//s6tfCVKPU9p83QJjWxui7kmxUghHD0JYYdkjDVpa7wVW2W69CxjnnZXmesrdi3G3a8lSxH2r4Gtmfut62GbSvr/rnTP5+c59JzRHLrPiz5VlctT4ywdUVynbRVzybNQ0qLk9f6js00D5kGw85Kzl+S2gJDSMMcurICkpmjAx1d1do0ZiMSScdhQy2BDaAqwg3TRhx+Tle1c706d8ix4UZ7EePBc0QODWXb1yTnkVTXoRv0HA79xyWv1dYdKysXFj4A+3YeHAtZyYehniNqhrmeI6HHMGdsmtvODTWbh+zK9PnqNhROuiwJaYXnJOf+SG1RG1q+1iz2B7MWPHOkpmFok5rIpl2l/OeMN+tcgT+oewHfvPDEZq1JKavYBzvWZoLYIaFs+5rkulkHhOQf4x7D4YQLoWdh8rhH5r5jz4ObHumb13EfSZoEbFsFW1cm99tWJbNzC38P+4oPec8h0GtEzTDXc0Tynrn5TfanaTdKttdsHrIlc3Hhgp7JUsf9rfh7FLbu80ik+jKENFxzdidVi2FokxpZaXklv3xhNT+duYKyyiqmndCbl1dvo7T8YNMPm2q0UTEmH8oPhLL9wWxNcr9zPTVOpM/tmHw47zkCRp1fM5R1H1L/Wa+jfejZ30xgyJTa6z00zG1bBa8/dEjDgEyI7Fl4MMjtn63rMTwJiTpceUlyLtr+mbSN85PmIbkdk2uknfrpJKj1O8XmIWq/DCHSUXlOm9RIYow8sWgjP3jyDdbvKOF9Y/vxD5ecRGHvTjbVaEsqK5LzL2pbwrh9Tc2liJC0td4fxA6dLevct2XPpuzddvCcuf2hbn/AK9lWc9uug6rNzlVbetmjsP7XamoLKisyzUOezTQPmQ2V+5LGBYNPPziTNmiy5xZKkjynTWpOC9bt4HuPL2HeW9s5sX8XfnftGZw1qveB16/I/itXdLgZ8ougw2DI/g7gt4ot1r5ddZ9bVrwu03o6Izsvae7RYzgMnVozlPUYBnmt+LpyHXsmt8GnHf5ayfZMkFtdM8y98UTS+ru6LgMzYa7wkPPoRrTuvw8ks5Wblx2cSVvz/MHg3m8cTPl8EtSGnQUdOqdbqySp1TK0ScdhY3EJt/5pGQ+9tp7enfO45cpxfGTykJrXJ/MaWi1PVRXs2ljHbNnqzIViqynokQSxgZPglCtrzpx1GZC0gW5vCnokbecH1RbodiR/x22rYOuqg7N0b/4pafVdXef+mZm5Q8+jK4QOXZrnd2mo4qKazUN2v52M9xgOJ38wmUkbfk5yAWFJkhqBoU06BnvLKrh71irufG4lVRGunzaSL00bSZf83MM3fvrmmg0iIHn++N/ChtcgJz85H6iu+yO9lp3XspfXpam8BLa/VUswW52MV+47uO3+joo9hsNJlx4yWzYcCrqn8Ru0XgXdoWBSEnIPVbqz2vlz1W7L/5x0z6yuU9/DZ+b235qz5f3ebckM2qrnkmWP+y+x0KlP0tnxQPOQ4c1XkySpXTG0SQ1QVRX544L1/PCpZby9s5T3jxvATRefyJCetZyzU1kOK/5S+/VnAMr2wKu/SToGVh35Itl1C9VCXMeku19OQeb+SGHw0G3ruq8eHAuSxhjNFRKPdjHMGGHPlrpny3ZtrLm/vM5JEOt9QtKNsUbTj6GQXUvgVuPL7woDJya3Q+1fllq9Icq21bDiadj925rbdupTs7tl9aWX+d2OXseRjq+yvbD2pcxM2rOwcSEQk2No2Nlw+jVJK/6+Y/3SRJLULGxEItXTK29t4+bHlrCgqJhxg7rxTx8Yy5TCnjU3ihE2LoAF98Oi3yfn9oSspFvcoapfOLSyIglv5aWH3GduFaX1uz/qNpn9VpYd418hZILcEYLe/oBXn1CY27H2ULl8BvzpH2q2wM/OhcJpSXDc3/SjbHfN8roMrL3hR89C6NjLD9itWdmeWpqiZO73X9tsv469Du9w2TPTpbOgR+2XSMjuACdclCyNLZqT/DeSlZt03Nx/UetBpxruJUmNqr6NSAxt0lEUbd/LLU+9weMLN9Kvawe+deGJfHDSILKqn7e2cyMsmp6EtU1LkmWLYy6GCR9PGjY88bXar6GV1jltVZUNC4MVpVC+t5ZQWVq/basvRTxevcccHsx6FiazZbadb5/K9lY7h25lzWWXO9fX3LagZxL26/riov/4ZKnjiGkw9MzW3yhFktSi2T1SOk6791Xw05kr+MULq8kK8JXzR/PF94ygY17mP5uyvbDsSZj/O1g1M5lNGzwF3v9fSbOKgh4Hd5aV3bIuHJqVnXSya65udlVVSZg74gxhtTD42Ffr2FGAG+c0T81qPfI6Qr+Tk9uhykuSWdnqYe6VX9WxowBffL4pK5Uk6ZgY2qRDVFZFHnxlHbfOeJMtu/dxxcSBfOuiExnYvSAJH2v+Cgvug8WPQNmuZJnju78O46+C3qNq32l7v3BoVlbywbq+1+ua9R+1nwvYbXDj1qW2L7cA+p6U3Par61xTjy9JUgtlaJOqeWnlVr73+BKWbNzJqUO78/NPn8akoT2Sb+dn3p8sf9zxVtKQYOzlMOHqpDFBVlbapbct53/n8HOOcguScel4eXxJkloZQ5sErNmyh397cin/t+QdBnUv4LarJ3HpCQWEJX+EP98H614GQnKey7n/CCd9wHNdmtL+WcmWtKRUbYfHlySplbERidq14pJybn9mOb9+cQ252VncOG041w5YQ97rD8AbTyQNNHqPgYlXw7iPQrdBaZcsSZKkNsJGJNIRVFRWcd/cdfzoz2+yfW8ZXz55H1/sNpuOrzwEezYlHeZO+0yy/HHgJFvFS5IkKTWGNrU7s97czPefWMK2d9bz1T6v8dFuL1CwYklyTaYTLkyC2ugLICcv7VIlSZIkQ5vajxWbdvPDx+eTt2IG381/kakFr5G1qxIGngqX/AecfCV06pV2mZIkSVINhja1edt37+OhRx+m49Lp/GfWS3TN20vsNIAw4SvJrFqfMWmXKEmSJNXJ0KY2q2zLGhY9eRe9Vz3ENbxNWW4+VSdeCqd9glB4TnKBaUmSJKmFM7SpbSndSVzyCDte+g09Ns/hNGBJhwlsOOtbDJz6UejQJe0KJUmSpAYxtKn1q6qEVc/CgvupWvooWRWlbK/qz0P5n+CkC6/lzFMnEez+KEmSpFbK0KbWa9NSWHAfLJwOuzZSkt2Zh8rO5qns8zj/gkv49JnDyc3OSrtKSZIk6bgY2tS67NkCix5MwtrG+cSQzVs9z+a2eDV/KpnIR6eO4vb3jqZ7R9v1S5IkqW0wtKnlq9gHb85Igtry/4OqCmL/8Swd/w/ctPwEFq7P47wT+/LoJScxqm/ntKuVJEmSGpWhTS1TjLD+VVjwO3j9D1CyHTr3g6nXs3zApfzji1XMmbONMf268JtrTuLdo/ukXbEkSZLUJAxtalmKi2DhA7DgftjyJuTkw4kfgAlX806fqdz655X8YWYRPTrm8f0rTuGq04eQ43lrkiRJasMMbUrfvt2w9LFk+ePqWUCEoWfBZV+GsZdTmt2Zn89axc9+8wLllVV8/t0juOHcUXQryE27ckmSJKnJGdqUjqoqWPN8EtSWPArle6DHcJh2E4z/GPQsJMbIows28MOn5rGhuJQLT+7H3198EsN7d0q7ekmSJKnZGNrUvLYsT4LaggdgZxF06ArjPgQTPg5Dp0Lmemqvrt3O9x5fwmtrd3DywK7850cnO8+cWAAAIABJREFUcubIXikXL0mSJDU/Q5ua3t5tSTORBffD+nkQsmDk+fC+f4ET3w+5BQc23bCjhB/+6Q3+OH8Dfbp04N8/NJ4PnTaY7Cwvji1JkqT2ydCmplFZDsv/nHR/XPYnqCqHvifDBd+HcR+BLv1rbL5nXwV3PbeSu2atIgI3nDuS66eNonMHD1FJkiS1b34i1rFZOB2evjnp9thtMJz/nSSMbZyfzKgt+j3s3Qqd+sCU62DCVdB/3IHlj/tVVUX+8GoRt85YxqZd+7h0wkC+fdEYBvfomNIvJkmSJLUshjY13MLp8NhXoLwkeV68Dh75Evz5n2HXBsjOgzGXwISrYdT5kF17l8c5q7fxvceXsGh9MROGdOdnnzyV04b1bMZfRJIkSWr5DG1quKdvPhjY9qsqh71b4AM/gpM/CAU96vzxddv28oOnlvLkorfp3zWfH39sIpdNGEiW561JkiRJhzG0qeGKi2ofryyHyX9T54/tKi3n9pkr+NULa8jOCnztvSdw3TkjKMjLbqJCJUmSpNbP0KaG6zY4WRJZ23gtKqsiD8xdx3/9eRlbdpdx5amD+NaFJ9K/W34TFypJkiS1foY2Ndz534FHroeqioNjuQXJ+CH+umIL33t8CW+8vYvJw3rwy8+czoQh3ZuxWEmSJKl1M7Sp4cZeAU98HSrLoGLfwe6R4z96YJNVm3fzb08u5S9LNzGoewF3fPxULhnXnxA8b02SJElqCEObGm7JH2HfTvjEH2D0e2u8VLy3nP9+ejn/89IaOuRk8a2LxvA3ZxeSn+t5a5IkSdKxMLSp4Wbfya5Ow7n4QVhf/AQDuxfwd+8bze59lfzoL29SXFLOxyYP4e8uOIG+XTxvTZIkSToehjY1TNE8WD+PH1d9jqKyfQCs31HCN36/kAicOaIX//SBsYwd2DXdOiVJkqQ2wtCmhpl9F3so4P6yd9UYjkDPTnn87vNneN6aJEmS1Iiy0i5Arciut2HxwzxQ8R72UHDYy9v3lBnYJEmSpEZWr9AWQrgohLAshLAihHDTEbb7cAghhhAmN16JajHm/QqqKvhTx8tqfXlg98ODnCRJkqTjc9TQFkLIBu4ALgbGAleHEMbWsl0X4CvA7MYuUi1AxT6Ydw+MvoCPXzyN7KyaM2oFudl888IxKRUnSZIktV31mWmbAqyIMa6KMZYB9wOX17Ld94B/B0obsT61FIsfgT2b4IwvMHVEL4iRjnnZBGBQ9wJ+cOU4rpg0KO0qJUmSpDanPo1IBgHrqj0vAs6ovkEIYRIwJMb4eAjhG3XtKIRwHXAdwNChQxterdIRI8z+GfQ+AUaex92PL4UQmPG35zCkZ8e0q5MkSZLatPrMtNXWWSIeeDGELOBHwNePtqMY490xxskxxsl9+vSpf5VKV9E82PAaTLmOLXvK+N2ct7hi4iADmyRJktQM6hPaioAh1Z4PBjZUe94FOAV4NoSwBpgKPGozkjZk9p3QoStMuJpfvrCafRVVfOnckWlXJUmSJLUL9Qltc4HRIYTCEEIecBXw6P4XY4zFMcbeMcbhMcbhwMvAZTHGeU1SsZrXzo2w5BGY9Cl2VObxPy+u4f3jBjCyT+e0K5MkSZLahaOGthhjBXAjMANYCkyPMS4OIdwcQqi997vajnn3QFUlTLmWX7+4hj1lldxw7qi0q5IkSZLajfo0IiHG+CTw5CFj36lj22nHX5ZahP1t/k+4iF0dh/Crv87kfWP7cdKArmlXJkmSJLUb9bq4ttqp1x+CvVvgjC/wvy+vpbiknBudZZMkSZKalaFNtYsxaUDSewwlg9/NL55fxTkn9GHCkO5pVyZJkiS1K4Y21a5oLmycD2d8gfvmrmPrnjK+fJ6zbJIkSVJzM7SpdrPvhA7d2HfyR7hr1krOKOzJ6cN7pl2VJEmS1O4Y2nS4nRtgyR/h1E/x4KLtvLNzHzc6yyZJkiSlwtCmw2Xa/Jefdg0/e3YlE4Z0512jeqddlSRJktQuGdpUU3kpzPsVjLmYR9/Ko2h7CV8+dxQhhLQrkyRJktolQ5tqWpy0+a88/Qvc8ewKThrQlfNP6pt2VZIkSVK7ZWjTQTHCyz+DPify1J7RrNq8hxudZZMkSZJSZWjTQetmw9sLqZryBW6fuZKRfTpx0Sn9065KkiRJatcMbTpo9p2Q342ZHc7ljbd3ccO5o8jOcpZNkiRJSpOhTYni9bDkUeKkT3PbrPUM6VnAZRMGpl2VJEmS1O4Z2pSY90sgMqfPh1hQVMyXpo0iJ9vDQ5IkSUqbn8oF5SWZNv+X8J9zShnQLZ8rTx2UdlWSJEmSMLQJ4PU/QMk2lg69mjlrtvGFc0bQISc77aokSZIkYWhTjEkDkr5j+bclvendOY+rpgxNuypJkiRJGYa29m7tS/D2ItaO/hTPr9jKte8eQX6us2ySJElSS2Foa+9m3wn53bmlaBzdCnL55NRhaVckSZIkqRpDW3tWXARLH2fLmKt5ctlO/ubsQjp3yEm7KkmSJEnV+Am9PZubtPn/753n0LlDNp89a3jaFUmSJEk6hDNt7VV5Cbzya3YXXsT/vhH59JnD6NYxN+2qJEmSJB3C0NZeLXoQSrZxb+UFdMjJ4pp3FaZdkSRJkqRaGNraoxhh9l2U9TqJ/1rel0+cMYxenTukXZUkSZKkWhja2qO3XoR3FvFY/mVkhyyuO2dE2hVJkiRJqoOhrT2afSdV+T347pqT+Ojpg+nXNT/tiiRJkiTVwdDW3uxYB288zovd3k9J7MAXzhmZdkWSJEmSjsDQ1t7M/QUR+H/rp/LBSYMY0rNj2hVJkiRJOgJDW3tSthdevZdl3d/D2sqeXD/NWTZJkiSppfPi2u3Jot9DyXb+bc85fGD8QEb06Zx2RZIkSZKOwtDWXmTa/G/qNJpZW09gxrmj0q5IkiRJUj24PLK9WPMCbFrM7XvO54Kx/RnTv0vaFUmSJEmqB2fa2ovZd1KS040Hdk/l9+c5yyZJkiS1Fs60tQfb3yIue5L7Ks9j6gmDGD+4e9oVSZIkSaonQ1t7MPcXRAI/LzmPLzvLJkmSJLUqhra2rmwv8dX/4ZkwhWEjRjN5eM+0K5IkSZLUAIa2tm7RdELpDu4seR9fPm902tVIkiRJaiAbkbRlMRJfvpM3wwgqB5/BWSN7pV2RJEmSpAZypq0tW/M8YfNSfl72Pr58/mhCCGlXJEmSJKmBnGlrw+LLd1JMV5b3uZBzx/RNuxxJkiRJx8CZtrZq+xpY9hT/W3EuXzx/rLNskiRJUitlaGuj4pxfUAW80P1yLjy5f9rlSJIkSTpGLo9si8r2UDHvXmZUTuFj559BVpazbJIkSVJr5UxbGxQXPEBu+U6e7Hg5l44fmHY5kiRJko6DM21tTYzsfeEOVlUN55zz3k9OtrlckiRJas38RN/GxFXP0al4BY/kXcqVpw1JuxxJkiRJx6leoS2EcFEIYVkIYUUI4aZaXv9iCGFRCGF+COGFEMLYxi9V9bF95k/YErsy/D2fIi/HTC5JkiS1dkf9VB9CyAbuAC4GxgJX1xLKfhdjHBdjnAj8O/BfjV6pjm7baroXPc0jWRfwkamj0q5GkiRJUiOoz1TMFGBFjHFVjLEMuB+4vPoGMcad1Z52AmLjlaj6eufp26mMWeSfdS35udlplyNJkiSpEdSnEckgYF2150XAGYduFEK4Afg7IA84r7YdhRCuA64DGDp0aENr1ZHs202XJffxlzCVD55zetrVSJIkSWok9Zlpq+0iX4fNpMUY74gxjgS+Dfy/2nYUY7w7xjg5xji5T58+DatUR7Rh1q/pGPewe8I1dOpgU1BJkiSprahPaCsCqrchHAxsOML29wNXHE9RaqAYCXPv5vU4kgsuvDTtaiRJkiQ1ovqEtrnA6BBCYQghD7gKeLT6BiGE0dWevh9Y3ngl6miKXn2KAWVvsXb0p+jWMS/tciRJkiQ1oqOuo4sxVoQQbgRmANnAPTHGxSGEm4F5McZHgRtDCO8FyoHtwGeasmjVtGPmT8iP3Zh66bVplyJJkiSpkdXr5KcY45PAk4eMfafa4682cl2qp/WrFjN210u8MPBznNOtS9rlSJIkSWpkXn25lVv1xI+pJIuTLv3btEuRJEmS1AQMba3Yxk1bmLjlMZb0PI8+A4elXY4kSZKkJmBoa8VefeyndAklDHifq1MlSZKktsrQ1kpt2rmXE9fex9qCE+l70rvSLkeSJElSEzG0tVLPPDGdkWED+e+6AUJt1z+XJEmS1BYY2lqh7XvK6P/GvezM7kHfMz6WdjmSJEmSmpChrRV6+C/PMS28Svmkz0JOh7TLkSRJktSEDG2tzM7ScnJfu4cKcuj1ni+mXY4kSZKkJmZoa2Xuf34JV8SZ7B51KXTpn3Y5kiRJkppYTtoFqP72llWw7cVf0yWUwLQvp12OJEmSpGbgTFsr8ruX1/CRyqfY3WciDD4t7XIkSZIkNQNDWytRWl7JwuceZmTWRjq/+8a0y5EkSZLUTAxtrcTv563jg2WPUZbfB8ZennY5kiRJkpqJoa0VKK+s4omZz3Nu9gJyp14LOXlplyRJkiSpmRjaWoGHX1vPhXsfoyorlzD5b9IuR5IkSVIzsntkC1dZFfn1Mwt5MGcW4ZQroXPftEuSJEmS1IycaWvhHl+4gdOLZ9CREsIZX0i7HEmSJEnNzJm2FqyqKvLTZ97klx3+TBw4hTDINv+SJElSe+NMWwv2f0veYcCWFxlctcFZNkmSJKmdMrS1UDFGbp+5nOsL/kLs3N82/5IkSVI7ZWhroZ59czN7N7zBGZWvEk6/BrJz0y5JkiRJUgo8p60FijHyk6eX86WOzxDJI5z22bRLkiRJkpQSZ9paoJdWbWX52g1czrOEUz5km39JkiSpHTO0tUC3P7OCz3b8K7mVe8EGJJIkSVK7ZmhrYV55axsvrdzMtR3+AkPOgIGT0i5JkiRJUooMbS3M7c+s4AMFr9OtZJ2zbJIkSZJsRNKSvL6+mJnLNjNrwLNQMQBOuiztkiRJkiSlzJm2FuSOmSsYn/82Q7e/DLb5lyRJkoQzbS3Gm+/s4qnX3+aR4S/C5g5w2ufSLkmSJElSC+BMWwvx05kr6Ju3jwlbn4RxH4ZOvdMuSZIkSVILYGhrAdZs2cOjCzbwr8MXEMr3wpTr0i5JkiRJUgthaGsBfvbsSvKy4bzih2HomTBwYtolSZIkSWohDG0pW7+jhD+8WsQ/nVBEdvFbtvmXJEmSVIOhLWV3PbeSEOBDFU9A10Fw4gfSLkmSJElSC2JoS9GmnaXcP3cd148tJ3/tc7b5lyRJknQYQ1uKfv78Kioqq/h8/tOQ3QFO/WzaJUmSJElqYQxtKdm2p4z/fXktHzulC13e+D2M/wh06pV2WZIkSZJaGENbSu55YTUl5ZX8XZ+5UL4XptiARJIkSdLhDG0pKC4p594X13DJyX3os+ReGHoWDBifdlmSJEmSWiBDWwp+89Iadu2r4Nsj34IdtvmXJEmSVDdDWzPbs6+CX76wmvNO7Muw5b+xzb8kSZKkIzK0NbPfzV7L9r3lfH1iFax+Dk6/FrJz0i5LkiRJUgtlaGtGpeWV3P38Ks4e1YuT190HOflw6mfSLkuSJElSC2Zoa0bT561j8659fPWsvrDgfhhnm39JkiRJR2ZoayZlFVXc+exKJg/rwenbH4eKEhuQSJIkSTqqeoW2EMJFIYRlIYQVIYSbann970IIS0IIC0MIT4cQhjV+qa3bw68VsaG4lBunFRLm/hyGvQv6j0u7LEmSJEkt3FFDWwghG7gDuBgYC1wdQhh7yGavAZNjjOOBB4F/b+xCW7OKyip++uxKxg3qxnt4BXasdZZNkiRJUr3UZ6ZtCrAixrgqxlgG3A9cXn2DGOPMGOPezNOXgcGNW2br9vjCjby1dS83njeKMOcu6DYExlySdlmSJEmSWoH6hLZBwLpqz4syY3W5BniqthdCCNeFEOaFEOZt3ry5/lW2YlVVkdtnruCEfp15X6+tsHqWbf4lSZIk1Vt9QluoZSzWumEInwQmA7fW9nqM8e4Y4+QY4+Q+ffrUv8pWbMbit1mxaTc3nDuKrLl3Q04BnPrptMuSJEmS1ErUJ7QVAUOqPR8MbDh0oxDCe4F/BC6LMe5rnPJatxgjP3lmBYW9O/GB0QWw4AEY/1Ho2DPt0iRJkiS1EvUJbXOB0SGEwhBCHnAV8Gj1DUIIk4C7SALbpsYvs3WauWwTSzbu5PppI8me/xvb/EuSJElqsKOGthhjBXAjMANYCkyPMS4OIdwcQrgss9mtQGfg9yGE+SGER+vYXbuxf5ZtUPcCPjihH8z5OQx/N/Q7Oe3SJEmSJLUi9eqGEWN8EnjykLHvVHv83kauq9V7aeVWXlu7g+9dcQq5K2ZA8Tq46Ja0y5IkSZLUytTr4tpquJ88s4K+XTrwkdMGw+y7oNtQGHNx2mVJkiRJamUMbU1g3pptvLRqK9edM4L8rUthzfMw5VrIyk67NEmSJEmtjKGtCdw+cwU9O+Xx8TOGwpy7kjb/kz6VdlmSJEmSWiFDWyNbVFTMs8s2c827CulYsRMWTocJH7PNvyRJkqRjYmhrZLfPXE7X/Bw+feYwePVeqCiFKbb5lyRJknRsDG2NaNnbu5ix+B0+e3YhXXIDzPkFFJ4D/camXZokSZKkVsrQ1ojumLmCjnnZfO6s4bDsCdhZBGd8Me2yJEmSJLVihrZGsmrzbh5fuIFPTR1Gj055SZv/7kPhhIvSLk2SJElSK2ZoayQ/e3YludlZXPPuQnh7Ebz1V5hynW3+JUmSJB0XQ1sjWLdtLw+/tp6rpwylb5f8ZJYttyNM+mTapUmSJElq5QxtjeCuWSsJAa47ZwTs2QqLfg8TroKCHmmXJkmSJKmVM7Qdp3d2ljJ9XhEfPm0wA7sX2OZfkiRJUqMytB2nn89aRWVV5Pr3jILKCpj7CxgxDfqemHZpkiRJktoAQ9tx2Lp7H7+dvZbLJwxkaK+O8MbjsHO9bf4lSZIkNRpD23G456+rKa2o5EvnjkwGZt8FPYbD6AtSrUuSJElS22FoO0bFe8u598W3uOSUAYzq2wU2LoC1L9rmX5IkSVKjMrQdo3tfWsPufRXccO6oZGD23ZDbCSZ+ItW6JEmSJLUthrZjsHtfBff8dTXvPakvYwd2hT1bkjb/E6+Ggu5plydJkiSpDTG0HYPfvvwWO/aWH5xle+XXULkvWRopSZIkSY3I0NZApeWV/Pz5VbxrVG8mDe0BleUw95cw4lzoMybt8iRJkiS1MYa2Brp/zlq27C7jxvMys2xLH4NdG2zzL0mSJKlJGNoaYF9FJXfNWsXpw3twRmHPZHD2XdCj0Db/kiRJkpqEoa0BHnp1PRuLS7nxvNGEEGDDa7Du5Uybf/+UkiRJkhqfSaOeKiqr+NmzKxk/uBvnjO6dDO5v8z/JNv+SJEmSmoahrZ4eW7iBtdv2cuO5o5JZtt2b4fUHYeLHIb9b2uVJkiRJaqMMbfVQVRW5/ZkVnNi/C+89qV8y+OqvobLMNv+SJEmSmpShrR7+tPhtVm7eww3njiIrKxxs8z/yfOhzQtrlSZIkSWrDctIuoKWLMfKTZ1YwoncnLhk3IBlc+ijs2giX3pZucZIkSVIrVl5eTlFREaWlpWmX0qTy8/MZPHgwubm5x/TzhrajeOaNTSzduJP/+MgEsrNCMjj7Lug5Aka9N93iJEmSpFasqKiILl26MHz48KRvRBsUY2Tr1q0UFRVRWFh4TPtweeQR7J9lG9yjgMsnDkwG178K62bDlC/Y5l+SJEk6DqWlpfTq1avNBjaAEAK9evU6rtlEU8cR/HXFVuav28H100aSm535U825G/I6J10jJUmSJB2XthzY9jve39HQdgQ/eWY5/bp24MOnDU4Gdm+C1/8AEz8B+V3TLU6SJElSu2Boq8Oc1duYvXob150zkg452cngK7+2zb8kSZKUkkdeW8/ZtzxD4U1PcPYtz/DIa+uPa387duzgpz/9aYN/7pJLLmHHjh3H9d4NYWirw+0zV9CrUx5XTxmSDFSUJW3+R70Peo9KtzhJkiSpnXnktfX8/UOLWL+jhAis31HC3z+06LiCW12hrbKy8og/9+STT9K9e/djft+GsntkLRas28GsNzfzrYvG0DEv8yda+ijsfhvOuCPd4iRJkqQ26F8eW8ySDTvrfP21tTsoq6yqMVZSXsm3HlzIfXPW1vozYwd25Z8vPbnOfd50002sXLmSiRMnkpubS+fOnRkwYADz589nyZIlXHHFFaxbt47S0lK++tWvct11yYq74cOHM2/ePHbv3s3FF1/Mu971Ll588UUGDRrEH//4RwoKCo7hL1A3Z9pqcfvMFXTNz+FTU4cdHJx9J/QcCSPPS68wSZIkqZ06NLAdbbw+brnlFkaOHMn8+fO59dZbmTNnDv/6r//KkiVLALjnnnt45ZVXmDdvHrfddhtbt249bB/Lly/nhhtuYPHixXTv3p0//OEPx1xPXZxpy3jktfXcOmMZGzLTrReO7UeX/MzF74pegaK5cPG/2+ZfkiRJagJHmhEDOPuWZ1i/o+Sw8UHdC3jgC2c2Sg1TpkypcS212267jYcffhiAdevWsXz5cnr16lXjZwoLC5k4cSIAp512GmvWrGmUWqozgXD4+liA55ZvPrg+ds5dkNcFJlydWo2SJElSe/bNC8dQkJtdY6wgN5tvXjim0d6jU6dOBx4/++yz/OUvf+Gll15iwYIFTJo0qdZrrXXo0OHA4+zsbCoqKhqtnv0MbcCtM5ZRUl7zZMPS8ipunbEMdr0Drz8Ek2zzL0mSJKXlikmD+MGV4xjUvYBAMsP2gyvHccWkQce8zy5durBr165aXysuLqZHjx507NiRN954g5dffvmY3+d4uTwS2FDLNOuB8Vd+BVXltvmXJEmSUnbFpEHHFdIO1atXL84++2xOOeUUCgoK6Nev34HXLrroIu68807Gjx/PmDFjmDp1aqO9b0MZ2oCB3QtqXR87tFtO0uZ/9AXQa2QKlUmSJElqSr/73e9qHe/QoQNPPfVUra/tP2+td+/evP766wfGv/GNbzR6feDySKDu9bH/efJq2LMJzvhCSpVJkiRJau8MbdS9PnbyO7+HXqNhhG3+JUmSJKXD5ZEZh62PLZoH6+fBJf9hm39JkiRJqTGN1GX2XdChK0y4Ku1KJEmSJLVj9QptIYSLQgjLQggrQgg31fL6OSGEV0MIFSGEDzd+mc1s19uw+GGY9Eno0CXtaiRJkiS1Y0cNbSGEbOAO4GJgLHB1CGHsIZutBT4L1N56pbWZ9yuoqoDTr027EkmSJEntXH1m2qYAK2KMq2KMZcD9wOXVN4gxrokxLgSqmqDG5lWxD+bdAydcaJt/SZIkqSVZOB1+dAp8t3tyv3B6s759586dm/X99qtPI5JBwLpqz4uAM47lzUII1wHXAQwdOvRYdtF0Fk6Hp2+G4syv2ufEdOuRJEmSdNDC6fDYV6A8c33l4nXJc4DxH02vrmZQn9AWahmLx/JmMca7gbsBJk+efEz7aBKHHgAAc+6Cfie3+QNAkiRJahGeugneXlT360VzoXJfzbHyEvjjjfDKvbX/TP9xcPEtde7y29/+NsOGDeNLX/oSAN/97ncJITBr1iy2b99OeXk53//+97n88svr3EdzqM/yyCJgSLXng4ENTVNOSp6+uWZgg+T50zenU48kSZKkmg4NbEcbr4errrqKBx544MDz6dOn87nPfY6HH36YV199lZkzZ/L1r3+dGNOdb6rPTNtcYHQIoRBYD1wFfLxJq2puxUUNG5ckSZLUuI4wIwYk57AVrzt8vNsQ+NwTx/SWkyZNYtOmTWzYsIHNmzfTo0cPBgwYwNe+9jVmzZpFVlYW69ev55133qF///7H9B6N4agzbTHGCuBGYAawFJgeY1wcQrg5hHAZQAjh9BBCEfAR4K4QwuKmLLrRdRvcsHFJkiRJzev870BuQc2x3IJk/Dh8+MMf5sEHH+SBBx7gqquu4re//S2bN2/mlVdeYf78+fTr14/S0tLjeo/jVZ+ZNmKMTwJPHjL2nWqP55Ism2ydzv/O4ee0NcIBIEmSJKmR7O818fTNyYq4boOTz+vH2YPiqquu4vOf/zxbtmzhueeeY/r06fTt25fc3FxmzpzJW2+91QjFH596hbY2r4kOAEmSJEmNaPxHG/0z+sknn8yuXbsYNGgQAwYM4BOf+ASXXnopkydPZuLEiZx4Yvpd5Q1t+zXBASBJkiSp5Vu06GDXyt69e/PSSy/Vut3u3bubq6Qa6tM9UpIkSZKUEkObJEmSJLVghjZJkiRJqUn7GmjN4Xh/R0ObJEmSpFTk5+ezdevWNh3cYoxs3bqV/Pz8Y96HjUgkSZIkpWLw4MEUFRWxefPmtEtpUvn5+QwefOxXSDO0SZIkSUpFbm4uhYWFaZfR4rk8UpIkSZJaMEObJEmSJLVghjZJkiRJasFCWp1aQgibgbdSefMj6w1sSbsItVkeX2pqHmNqSh5fakoeX2pKLfX4GhZj7HO0jVILbS1VCGFejHFy2nWobfL4UlPzGFNT8vhSU/L4UlNq7ceXyyMlSZIkqQUztEmSJElSC2ZoO9zdaRegNs3jS03NY0xNyeNLTcnjS02pVR9fntMmSZIkSS2YM22SJEmS1IIZ2iRJkiSpBTO0VRNCuCiEsCyEsCKEcFPa9ajtCCEMCSHMDCEsDSEsDiF8Ne2a1PaEELJDCK+FEB5Puxa1LSGE7iGEB0MIb2T+P3Zm2jWp7QghfC3zb+PrIYT7Qgj5adek1i2EcE8IYVMI4fVqYz1DCH8OISzP3PdIs8aGMrRlhBCygTuAi4GxwNUhhLHpVqU2pAL4eozxJGAqcIPHl5rAV4GlaRehNum/gT/FGE8EJuBxpkYSQhgEfAWYHGM8Bcig9KOvAAAER0lEQVQGrkq3KrUBvwYuOmTsJuDpGONo4OnM81bD0HbQFGBFjHFVjLEMuB+4POWa1EbEGDfGGF/NPN5F8oFnULpVqS0JIQwG3g/8Iu1a1LaEELoC5wC/BIgxlsUYd6RbldqYHKAghJADdAQ2pFyPWrkY4yxg2yHDlwP3Zh7fC1zRrEUdJ0PbQYOAddWeF+GHajWBEMJwYBIwO91K1Mb8GPgWUJV2IWpzRgCbgV9llt/+IoTQKe2i1DbEGNcD/wGsBTYCxTHG/0u3KrVR/WKMGyH5Mh3om3I9DWJoOyjUMub1ENSoQgidgT8Afxtj3Jl2PWobQggfADbFGF9Juxa1STnAqcDPYoyTgD20smVFarky5xVdDhQCA4FOIYRPpluV1PIY2g4qAoZUez4Yp+fViEIIuSSB7bcxxofSrkdtytnAZSGENSRLu88LIfxvuiWpDSkCimKM+1cHPEgS4qTG8F5gdYxxc4yxHHgIOCvlmtQ2vRNCGACQud+Ucj0NYmg7aC4wOoRQGELIIzkJ9tGUa1IbEUIIJOeDLI0x/lfa9ahtiTH+fYxxcIxxOMn/u56JMfpNtRpFjPFtYF0IYUxm6HxgSYolqW1ZC0wNIXTM/Ft5Pja6UdN4FPhM5vFngD+mWEuD5aRdQEsRY6wIIdwIzCDpXHRPjHFxymWp7Tgb+BSwKIQwPzP2DzHGJ1OsSZLq68vAbzNfaq4CPpdyPWojYoyzQwgPAq+SdFp+Dbg73arU2oUQ7gOmAb1DCEXAPwO3ANNDCNeQfFnwkfQqbLgQo6dtSZIkSVJL5fJISZIkSWrBDG2SJEmS1IIZ2iRJkiSpBTO0SZIkSVILZmiTJEmSpBbM0CZJavVCCJUhhPnVbjc14r6HhxBeb6z9SZLUUF6nTZLUFpTEGCemXYQkSU3BmTZJUpsVQlgTQvhhCGFO5jYqMz4shPB0CGFh5n5oZrxfCOHhEMKCzO2szK6yQwg/DyEsDiH8XwihILVfSpLU7hjaJEltQcEhyyM/Vu21nTHGKcDtwI8zY7cD/xNjHA/8FrgtM34b8FyMcQJwKrA4Mz4auCPGeDKwA/hQE/8+kiQdEGKMadcgSdJxCSHsjjF2rmV8DXBejHFVCCEXeDvG2CuEsAUYEGMsz4xvjDH2DiFsBgbHGPdV28dw4M8xxtGZ598GcmOM32/630ySJGfaJEltX6zjcV3b1GZftceVeE64JKkZGdokSW3dx6rdv5R5/CJwVebxJ4AXMo+fBq4HCCFkhxC6NleRkiTVxW8KJUltQUEIYX6153+KMe5v+98hhDCb5IvKqzNjXwHuCSF8E9gMfC4z/lXg7hDCNSQzatcDG5u8ekmSjsBz2iRJbVbmnLbJMcYtadciSdKxcnmkJEmSJLVgzrRJkiRJUgvmTJskSZIktWCGNkmSJElqwQxtkiRJktSCGdokSZIkqQUztEmSJElSC/b/AfrSRG3BoVE/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch/layer normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check\n",
    "\n",
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-7 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.3004790897684924\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.052114776533016\n",
      "W1 relative error: 3.90e-09\n",
      "W2 relative error: 6.87e-08\n",
      "W3 relative error: 2.13e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.57e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "  \n",
    "  # Most of the errors should be on the order of e-7 or smaller.   \n",
    "  # NOTE: It is fine however to see an error for W2 on the order of e-5\n",
    "  # for the check when reg = 0.0\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the **learning rate** and **weight initialization scale** to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment2\\cs231n\\layers.py:30: RuntimeWarning: overflow encountered in matmul\n",
      "  out = x @ w + b\n",
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment2\\cs231n\\layers.py:799: RuntimeWarning: invalid value encountered in subtract\n",
      "  shifted_logits = x - np.max(x, axis=1, keepdims=True)\n",
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment2\\cs231n\\classifiers\\fc_net.py:305: RuntimeWarning: overflow encountered in square\n",
      "  loss += 0.5 * self.reg * np.sum(self.params['W' + str(i + 1)] ** 2)\n",
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment2\\cs231n\\classifiers\\fc_net.py:305: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  loss += 0.5 * self.reg * np.sum(self.params['W' + str(i + 1)] ** 2)\n",
      "D:\\Codes\\PycharmProjects\\CS231nAssignment\\assignment2\\cs231n\\layers.py:123: RuntimeWarning: invalid value encountered in less\n",
      "  dx[x < 0] = 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight_scale 0.100000 learning_rate 0.010000 \n",
      "[0.14, 0.12, 0.1, 0.04, 0.12, 0.16, 0.12, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08]\n",
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      "weight_scale 0.010000 learning_rate 0.010000 \n",
      "[0.22, 0.24, 0.46, 0.56, 0.62, 0.62, 0.82, 0.8, 0.86, 0.98, 0.94, 0.98, 0.98, 0.96, 0.98, 0.98, 0.98, 1.0, 1.0, 1.0, 1.0]\n",
      "[0.091, 0.096, 0.152, 0.121, 0.165, 0.158, 0.173, 0.171, 0.172, 0.198, 0.183, 0.177, 0.196, 0.169, 0.177, 0.187, 0.186, 0.18, 0.184, 0.178, 0.177]\n",
      "weight_scale 0.100000 learning_rate 0.001000 \n",
      "[0.22, 0.38, 0.32, 0.66, 0.78, 0.88, 0.9, 0.96, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "[0.119, 0.15, 0.156, 0.153, 0.172, 0.174, 0.176, 0.179, 0.179, 0.179, 0.179, 0.179, 0.172, 0.172, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17]\n",
      "weight_scale 0.010000 learning_rate 0.001000 \n",
      "[0.1, 0.1, 0.14, 0.22, 0.28, 0.4, 0.46, 0.42, 0.5, 0.52, 0.54, 0.54, 0.56, 0.58, 0.6, 0.58, 0.6, 0.62, 0.6, 0.56, 0.58]\n",
      "[0.06, 0.058, 0.073, 0.083, 0.096, 0.106, 0.109, 0.105, 0.117, 0.124, 0.124, 0.126, 0.121, 0.122, 0.124, 0.118, 0.121, 0.122, 0.126, 0.131, 0.128]\n"
     ]
    },
    {
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Q2fgporNuTh4YGIjBwcGiwzAz6yiSlkfEQKN2rmFkZmYVuSUFSQdJuk3SWklrJJ1Tpc0Rkn4k6Q+S/i6vWMzGyqW5rVc1MyV1vHYA50fECkl7Assl3RIR949o8xjwfrL1n81KodHNb2bdrJlFdo6p8nOwpLrvjYiNEbEiPd4KrCVbj2Fkm0cj4ifA9hb+BrMJ5dpI1suauVK4AjgWWEN2n8KRwH3A3pIWRMStjXYgaSYwm2zVtjGTtABYADBjxozx7MKsaa6NZL2smTGFnwMvjYhjI+IlwEuBVcBc4NON3pzWX7gBODcitownyIi4PCIGImJg+vTp49mFWdNaLc1t1smaSQpHRsS9w08iYjVZgbwHGr1R0mSyhHBNRCwZf5hm7eOb36yXNdN99KCkzwP/np6fATwg6Vlkg8lVSRJZ19PaiLik5UjN2sQ3v1kva3jzmqRnA+8DTiAbU7gD+Dzwe2BKRDxe430nAP8JrAaeSpsvBGYARMRlkvYHBoG9UpsngKPqdTP55jUzs7Fr9ua1ZgriPQl8Mv2MVjUhpPfdQZZE6u37YeDARjGYmVl7NLPy2vHAR4GDR7aPiBfmGJeZmRWgmTGFLwMfAJYDOxu0NTOzDtZMUtgSEd/KPRIzMytcM0lhmaSLgCXAH4Y3jpymamZm3aGZpHDCqN/g9RTMzLpSM7OPXtmOQMzMrHg1k4KkN0bEdZLeX+31iPiX/MIysyIsXTnkm/Z6XL0rhX3SbxcbMusBLhluUCcpRMQX0u8Pty8cMytKvZLhTgq9o5mb16YBbwdmsuvNawvyC8us3PLsZimqC8clww2am330TeDHZDWPfPOa9bw8u1mK7MI5YGofQ1USgEuG95ZmSmc/JyLOj4hrI+L64Z/cIzMrqTxXZity1TeXDDdoLil8V9Jrco/ErEPk2c1SZBfOvNn9XDT/aPqn9iGgf2ofF80/2uMJPaaZ7qOzgQ9KehL4I1nl04iIfXONzKyk8uxmaUcXTr0xi3mz+50EelwzVwrTgMnA3mTTU6fhaarWw/LsZpmIfS9dOcScxcs4ZNG3mbN4GUtXDu3y2gVLVjO0eRvB02MWI9tYb6t389rhEfFz4EU1mrj2kfWkPFdma3XfjQaqPe3UGqnXfbQIeAfwr1Vec+0j62l5drM02ne97p9GJ31PO7VG6t289o7027WPzEqi0ZVAo5N+3mMWLpPR+ZoZU0DSEZLmS3rT8E/egZnZMzWaslrr5D68Pc/xEI9XdIeGSUHSh4DLgcuAU4DPAqfnHJeZVdHoSqDRST/PaadF3mNhE6eZKalnAMcCKyLiTEnPB76Yb1hmVk2j7p9mBqrzGg/xeEV3aCYpbIuInZJ2SNoTeBg4NOe4zLpWK/3uC+fO2mVMAZ7Z/VPUvQYuk9EdmhlTWClpKnAlMAjcDazINSqzLtVqv3uZ7zp2mYzuoIio/aIkYP+I2JievwDYKyIKSwoDAwMxODhY1MebtWTO4mVVv033T+3jzkUnFRDRxPLso/KStDwiBhq1q9t9FBEh6Sbgpen5A2MI4CDgq8D+wFPA5RHxuVFtBHwOOBV4EnhbkQnHLG/d3u/uMhmdr5nuo7slHTeOfe8Azo+II4HjgfdIOmpUm1OAw9PPAuDScXyOWcdoNGXUrGg1k4Kk4auIE8gSwzpJKyStlNTw23xEbBz+1h8RW4G1wOivEKcBX43Mj4GpaXaTWVdyv7uVXb3uo7uB44B5rX6IpJnAbOCuUS/1A78Z8Xx92rZx1PsXkF1JMGPGjFbDMStMnnWTzCZCvaQggIh4sJUPkDQFuAE4NyK2VPuMUZ4x8h0Rl5PdQMfAwEDtkXGzknB5autU9ZLCdEnn1XoxIi5ptHNJk8kSwjURsaRKk/XAQSOeHwhsaLRfszIrcklNs1bVG2ieBEwB9qzxU1eaWXQFsLZOArkReIsyxwOPD09/NetULvdgnazelcLGiPjHFvY9BzgTWC1pVdp2ITADICIuA75DNh31AbIpqWe18HlmpdDt006tuzUcUxiviLij0T4iu3PuPa18jlnZuNyDdbJ63Ud/0bYozLqIp51aJ6u3yM5j7QzErFt42ql1smaqpJrZGHXrtFPXNup+Tgpm1hRPte0NTgpm1pR6U22bTQq+0ig/JwUza0qrU219pdEZmqmSambWcoXXom/qW7pyiDmLl3HIom8zZ/Gyphc26jVOCmbWlFan2hZ5U1+rK971EicFM2tKq0uBFrmWRNFXKZ3EYwpm1rRWptounDtrlzEFaN9NfS490jxfKZhZW7R6pdEKr3jXPF8pmFnbFHVTX5FXKZ3GScHMup5LjzTPScHMekK3lh6ZaB5TMDOzCicFMzOrcPeRmZWGayMVz0nBzErBtZHKwd1HZlYKvuu4HJwUzKwUfNdxOTgpmFkp+K7jcnBSMLNSaLUKq00MDzSbWSn4ruNyyC0pSLoSeC3waES8uMrr+wBXAocBvwfeHhH35RWPmZWf7zouXp7dR1cBJ9d5/UJgVUQcA7wF+FyOsZhZh/PKae2RW1KIiNuBx+o0OQq4NbX9KTBT0vPyisfMOpdXTmufIgea7wHmA0h6OXAwcGCB8ZhZSfkehvYpMiksBvaRtAp4H7AS2FGtoaQFkgYlDW7atKmdMZpZCfgehvYpLClExJaIOCsijiUbU5gOPFSj7eURMRARA9OnT29rnGZWPN/D0D6FJQVJUyXtnp6+E7g9IrYUFY+ZlZfvYWifPKekXgecCEyTtB74KDAZICIuA44EvippJ3A/8I68YjGzzuZ7GNpHEVF0DGMyMDAQg4ODRYdhZtZRJC2PiIFG7XxHs5lZwcq0joSTgpl1hTKdWMeibOtIuCCemXW8Tr65rWz3YDgpmFnHK9uJdSzKdg+Gk4KZdbyynVjHomz3YDgpmFnHK9uJdSzKdg+Gk4KZdbyynVjHYt7sfi6afzT9U/sQ0D+1j4vmH+3ZR2Zm49XpN7eVaR0JJwUz6wplOrF2MncfmZlZhZOCmZlVOCmYmVmFk4KZmVU4KZiZWYVnH5mZlVw7i/05KZiZlVi7q6i6+8jMrMTaXezPScHMrMTaXezPScHMrMTaXezPScHMrMTaXezPA81mZiXW7mJ/TgpmZjlrdUppO4v9OSmYmbWo3km/3VNKW+UxBTOzFgyf9Ic2byN4+qS/dOUQ0HnrR+eWFCRdKelRSffVeH1vSd+SdI+kNZLOyisWM7O8NDrpd9r60XleKVwFnFzn9fcA90fES4ATgU9L2j3HeMzMJlyjk36nrR+dW1KIiNuBx+o1AfaUJGBKarsjr3jMzPLQ6KTfaetHFzmm8L+BI4ENwGrgnIh4qlpDSQskDUoa3LRpUztjNDOrq9FJf97sfi6afzT9U/sQ0D+1j4vmH13KQWYARUR+O5dmAjdFxIurvHY6MAc4DzgMuAV4SURsqbfPgYGBGBwcnPhgzczGqZ1VTMdL0vKIGGjUrsgpqWcBiyPLSg9Iegg4Ari7wJjMzKqqd+Jv530EeSuy++jXwF8ASHoeMAv4RYHxmJlV1WjaaTfJ7UpB0nVks4qmSVoPfBSYDBARlwEfB66StBoQ8MGI+G1e8ZiZ1VPvSqDetNNuuUIYlltSiIg3Nnh9A/CavD7fzKxZje467rR7DVrhO5rNrOc1ugGt0+41aIWTgpn1vEZXAp12r0ErnBTMrOc1uhLotHsNWuEqqWbW8xbOnbXLmAI880qgm6ad1uOkYGY9r90L2ZSZk4KZGb1zJdCIxxTMzKzCScHMzCqcFMzMrMJJwczMKpwUzMysItf1FPIgaRPwq3G+fRpQ1qJ7jm18yhwblDs+xzY+nRrbwRExvdEOOi4ptELSYDOLTBTBsY1PmWODcsfn2Man22Nz95GZmVU4KZiZWUWvJYXLiw6gDsc2PmWODcodn2Mbn66OrafGFMzMrL5eu1IwM7M6nBTMzKyiZ5KCpJMlrZP0gKRFRcczkqRfSlotaZWkwYJjuVLSo5LuG7FtX0m3SPp5+r1PiWL7mKShdOxWSTq1oNgOknSbpLWS1kg6J20v/NjVia3wYydpD0l3S7onxfYPafshku5Kx+16SbuXKLarJD004rgd2+7YRsQ4SdJKSTel560ft4jo+h9gEvAgcCiwO3APcFTRcY2I75fAtKLjSLG8CjgOuG/Etk8Bi9LjRcAnSxTbx4C/K8Fxez5wXHq8J/Az4KgyHLs6sRV+7AABU9LjycBdwPHA14A3pO2XAe8uUWxXAacX/f9cius84FrgpvS85ePWK1cKLwceiIhfRMQfgX8HTis4plKKiNuBx0ZtPg34Snr8FWBeW4NKasRWChGxMSJWpMdbgbVAPyU4dnViK1xknkhPJ6efAE4Cvp62F3XcasVWCpIOBP4S+FJ6LibguPVKUugHfjPi+XpK8o8iCeB7kpZLWlB0MFU8LyI2QnaCAZ5bcDyjvVfSval7qZCurZEkzQRmk32zLNWxGxUblODYpS6QVcCjwC1kV/WbI2JHalLYv9fRsUXE8HH7RDpun5H0rCJiAz4LfAB4Kj3fjwk4br2SFFRlW2kyPjAnIo4DTgHeI+lVRQfUQS4FDgOOBTYCny4yGElTgBuAcyNiS5GxjFYltlIcu4jYGRHHAgeSXdUfWa1Ze6NKHzoqNkkvBi4AjgBeBuwLfLDdcUl6LfBoRCwfublK0zEft15JCuuBg0Y8PxDYUFAszxARG9LvR4FvkP3DKJNHJD0fIP1+tOB4KiLikfQP9yng/1DgsZM0meyke01ELEmbS3HsqsVWpmOX4tkMfJ+s336qpOHlggv/9zoitpNTd1xExB+AL1PMcZsDvE7SL8m6w08iu3Jo+bj1SlL4CXB4GpnfHXgDcGPBMQEg6TmS9hx+DLwGuK/+u9ruRuCt6fFbgW8WGMsuhk+4yesp6Nil/twrgLURccmIlwo/drViK8OxkzRd0tT0uA94NdmYx23A6alZUcetWmw/HZHkRdZn3/bjFhEXRMSBETGT7Hy2LCL+hok4bkWPnrfrBziVbNbFg8DfFx3PiLgOJZsNdQ+wpujYgOvIuhK2k11hvYOsr/JW4Ofp974liu3fgNXAvWQn4OcXFNsJZJfq9wKr0s+pZTh2dWIr/NgBxwArUwz3AR9J2w8F7gYeAP4v8KwSxbYsHbf7gKtJM5SK+gFO5OnZRy0fN5e5MDOzil7pPjIzsyY4KZiZWYWTgpmZVTgpmJlZhZOCmZlVOClYz5L0RPo9U9KbJnjfF456/sOJ3L9ZXpwUzGAmMKakIGlSgya7JIWI+B9jjMmsEE4KZrAYeGWqjf+3qQjaxZJ+koqevQtA0olpXYJryW5eQtLSVMhwzXAxQ0mLgb60v2vStuGrEqV936dsDY0zRuz7+5K+Lumnkq5Jd8yatdVujZuYdb1FZOsKvBYgndwfj4iXpQqYd0r6Xmr7cuDFEfFQev72iHgslUH4iaQbImKRpPdGVkhttPlkBeheAkxL77k9vTYbeBFZvZo7yerb3DHxf65Zbb5SMHum1wBvSSWT7yIrVXF4eu3uEQkB4P2S7gF+TFZ08XDqOwG4LrJCdI8APyCrtjm87/WRFahbRdatZdZWvlIweyYB74uIm3fZKJ0I/Peo568GXhERT0r6PrBHE/uu5Q8jHu/E/z6tAL5SMIOtZMtUDrsZeHcqN42kF6YKtqPtDfxXSghHkJV8HrZ9+P2j3A6ckcYtppMtMXr3hPwVZhPA30TMsiqYO1I30FXA58i6blakwd5NVF/W8P8BZ0u6F1hH1oU07HLgXkkrIitpPOwbwCvIquIG8IGIeDglFbPCuUqqmZlVuPvIzMwqnBTMzKzCScHMzCqcFMzMrMJJwczMKpwUzMyswknBzMwq/j8YZme25lVvJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "weight_scales = [1e-1,1e-2]   # Experiment with this!\n",
    "learning_rates = [1e-2,1e-3]  # Experiment with this!\n",
    "for learning_rate in learning_rates:\n",
    "    for weight_scale in weight_scales:\n",
    "        model = FullyConnectedNet([100, 100],\n",
    "                      weight_scale=weight_scale, dtype=np.float64)\n",
    "        solver = Solver(model, small_data,\n",
    "                        print_every=10, num_epochs=20, batch_size=25,\n",
    "                        update_rule='sgd',verbose=False,\n",
    "                        optim_config={\n",
    "                          'learning_rate': learning_rate,\n",
    "                        }\n",
    "                 )\n",
    "        solver.train()\n",
    "        print(\"weight_scale %f learning_rate %f \"%(weight_scale,learning_rate))\n",
    "        print(solver.train_acc_history)\n",
    "        print(solver.val_acc_history)\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again, you will have to adjust the learning rate and weight initialization scale, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight_scale 0.100000 learning_rate 0.002000 \n",
      "[0.12, 0.14, 0.26, 0.5, 0.7, 0.66, 0.82, 0.84, 0.86, 0.96, 0.98, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "[0.119, 0.086, 0.128, 0.101, 0.119, 0.113, 0.104, 0.098, 0.101, 0.101, 0.101, 0.096, 0.097, 0.093, 0.094, 0.094, 0.094, 0.093, 0.093, 0.093, 0.093]\n",
      "weight_scale 0.100000 learning_rate 0.000200 \n",
      "[0.12, 0.16, 0.28, 0.38, 0.48, 0.54, 0.62, 0.66, 0.84, 0.82, 0.9, 0.96, 0.96, 0.94, 1.0, 1.0, 1.0, 1.0, 0.98, 1.0, 1.0]\n",
      "[0.09, 0.065, 0.112, 0.121, 0.1, 0.118, 0.108, 0.121, 0.098, 0.11, 0.106, 0.103, 0.112, 0.112, 0.109, 0.109, 0.111, 0.111, 0.105, 0.106, 0.106]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "weight_scales=[1e-1]\n",
    "learning_rates=[2e-3,2e-4]\n",
    "for learning_rate in learning_rates:\n",
    "    for weight_scale in weight_scales:\n",
    "        model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                        weight_scale=weight_scale, dtype=np.float64)\n",
    "        solver = Solver(model, small_data,\n",
    "                        print_every=10, num_epochs=20, batch_size=25,\n",
    "                        update_rule='sgd',verbose=False,\n",
    "                        optim_config={\n",
    "                          'learning_rate': learning_rate,\n",
    "                        }\n",
    "                 )\n",
    "        solver.train()\n",
    "        print(\"weight_scale %f learning_rate %f \"%(weight_scale,learning_rate))\n",
    "        print(solver.train_acc_history)\n",
    "        print(solver.val_acc_history)\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net? In particular, based on your experience, which network seemed more sensitive to the initialization scale? Why do you think that is the case?\n",
    "\n",
    "## Answer:\n",
    "Five layer net is more sensitive to initialization scale.In order to prevent it stuck in local minnmum,we should set the initialization value more carefully,especially in deep network.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.882347033505819e-09\n",
      "velocity error:  4.269287743278663e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "# Should see relative errors around e-8 or less\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.384572\n",
      "(Epoch 0 / 5) train acc: 0.103000; val_acc: 0.123000\n",
      "(Iteration 11 / 200) loss: 2.312321\n",
      "(Iteration 21 / 200) loss: 2.150762\n",
      "(Iteration 31 / 200) loss: 2.112645\n",
      "(Epoch 1 / 5) train acc: 0.249000; val_acc: 0.236000\n",
      "(Iteration 41 / 200) loss: 2.056032\n",
      "(Iteration 51 / 200) loss: 1.977760\n",
      "(Iteration 61 / 200) loss: 2.103483\n",
      "(Iteration 71 / 200) loss: 2.003576\n",
      "(Epoch 2 / 5) train acc: 0.295000; val_acc: 0.263000\n",
      "(Iteration 81 / 200) loss: 2.019338\n",
      "(Iteration 91 / 200) loss: 1.895378\n",
      "(Iteration 101 / 200) loss: 1.889854\n",
      "(Iteration 111 / 200) loss: 1.924155\n",
      "(Epoch 3 / 5) train acc: 0.355000; val_acc: 0.286000\n",
      "(Iteration 121 / 200) loss: 1.768830\n",
      "(Iteration 131 / 200) loss: 1.925380\n",
      "(Iteration 141 / 200) loss: 1.870257\n",
      "(Iteration 151 / 200) loss: 1.925160\n",
      "(Epoch 4 / 5) train acc: 0.329000; val_acc: 0.288000\n",
      "(Iteration 161 / 200) loss: 1.744298\n",
      "(Iteration 171 / 200) loss: 1.806632\n",
      "(Iteration 181 / 200) loss: 1.804669\n",
      "(Iteration 191 / 200) loss: 1.726709\n",
      "(Epoch 5 / 5) train acc: 0.378000; val_acc: 0.302000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.452439\n",
      "(Epoch 0 / 5) train acc: 0.088000; val_acc: 0.093000\n",
      "(Iteration 11 / 200) loss: 2.168232\n",
      "(Iteration 21 / 200) loss: 1.964541\n",
      "(Iteration 31 / 200) loss: 1.957637\n",
      "(Epoch 1 / 5) train acc: 0.353000; val_acc: 0.270000\n",
      "(Iteration 41 / 200) loss: 1.665146\n",
      "(Iteration 51 / 200) loss: 1.833819\n",
      "(Iteration 61 / 200) loss: 1.575295\n",
      "(Iteration 71 / 200) loss: 1.752660\n",
      "(Epoch 2 / 5) train acc: 0.409000; val_acc: 0.331000\n",
      "(Iteration 81 / 200) loss: 1.850844\n",
      "(Iteration 91 / 200) loss: 1.651562\n",
      "(Iteration 101 / 200) loss: 1.715215\n",
      "(Iteration 111 / 200) loss: 1.712802\n",
      "(Epoch 3 / 5) train acc: 0.480000; val_acc: 0.342000\n",
      "(Iteration 121 / 200) loss: 1.560639\n",
      "(Iteration 131 / 200) loss: 1.574364\n",
      "(Iteration 141 / 200) loss: 1.532105\n",
      "(Iteration 151 / 200) loss: 1.378587\n",
      "(Epoch 4 / 5) train acc: 0.537000; val_acc: 0.354000\n",
      "(Iteration 161 / 200) loss: 1.262166\n",
      "(Iteration 171 / 200) loss: 1.449104\n",
      "(Iteration 181 / 200) loss: 1.418370\n",
      "(Iteration 191 / 200) loss: 1.232782\n",
      "(Epoch 5 / 5) train acc: 0.570000; val_acc: 0.365000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\programs\\anaconda\\envs\\cs231na2\\lib\\site-packages\\matplotlib\\figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 5e-3,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.items():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=\"loss_%s\" % update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=\"train_acc_%s\" % update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=\"val_acc_%s\" % update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "**NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes. \n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.502645229894295e-08\n",
      "cache error:  2.6477955807156126e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.1395691798535431e-07\n",
      "v error:  4.208314038113071e-09\n",
      "m error:  4.214963193114416e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 2.572649\n",
      "(Epoch 0 / 5) train acc: 0.128000; val_acc: 0.128000\n",
      "(Iteration 11 / 200) loss: 2.101021\n",
      "(Iteration 21 / 200) loss: 1.950463\n",
      "(Iteration 31 / 200) loss: 1.959746\n",
      "(Epoch 1 / 5) train acc: 0.410000; val_acc: 0.336000\n",
      "(Iteration 41 / 200) loss: 1.769947\n",
      "(Iteration 51 / 200) loss: 1.715977\n",
      "(Iteration 61 / 200) loss: 1.741014\n",
      "(Iteration 71 / 200) loss: 1.729979\n",
      "(Epoch 2 / 5) train acc: 0.461000; val_acc: 0.351000\n",
      "(Iteration 81 / 200) loss: 1.384247\n",
      "(Iteration 91 / 200) loss: 1.582450\n",
      "(Iteration 101 / 200) loss: 1.472060\n",
      "(Iteration 111 / 200) loss: 1.326057\n",
      "(Epoch 3 / 5) train acc: 0.525000; val_acc: 0.360000\n",
      "(Iteration 121 / 200) loss: 1.692414\n",
      "(Iteration 131 / 200) loss: 1.467476\n",
      "(Iteration 141 / 200) loss: 1.383112\n",
      "(Iteration 151 / 200) loss: 1.363987\n",
      "(Epoch 4 / 5) train acc: 0.512000; val_acc: 0.382000\n",
      "(Iteration 161 / 200) loss: 1.421389\n",
      "(Iteration 171 / 200) loss: 1.156715\n",
      "(Iteration 181 / 200) loss: 1.263007\n",
      "(Iteration 191 / 200) loss: 1.166461\n",
      "(Epoch 5 / 5) train acc: 0.600000; val_acc: 0.368000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.446070\n",
      "(Epoch 0 / 5) train acc: 0.168000; val_acc: 0.161000\n",
      "(Iteration 11 / 200) loss: 2.178431\n",
      "(Iteration 21 / 200) loss: 2.081362\n",
      "(Iteration 31 / 200) loss: 1.865579\n",
      "(Epoch 1 / 5) train acc: 0.350000; val_acc: 0.324000\n",
      "(Iteration 41 / 200) loss: 1.763417\n",
      "(Iteration 51 / 200) loss: 1.762660\n",
      "(Iteration 61 / 200) loss: 1.852681\n",
      "(Iteration 71 / 200) loss: 1.855780\n",
      "(Epoch 2 / 5) train acc: 0.441000; val_acc: 0.344000\n",
      "(Iteration 81 / 200) loss: 1.485945\n",
      "(Iteration 91 / 200) loss: 1.682277\n",
      "(Iteration 101 / 200) loss: 1.519825\n",
      "(Iteration 111 / 200) loss: 1.559683\n",
      "(Epoch 3 / 5) train acc: 0.460000; val_acc: 0.352000\n",
      "(Iteration 121 / 200) loss: 1.720820\n",
      "(Iteration 131 / 200) loss: 1.522706\n",
      "(Iteration 141 / 200) loss: 1.501099\n",
      "(Iteration 151 / 200) loss: 1.406326\n",
      "(Epoch 4 / 5) train acc: 0.536000; val_acc: 0.364000\n",
      "(Iteration 161 / 200) loss: 1.309753\n",
      "(Iteration 171 / 200) loss: 1.497133\n",
      "(Iteration 181 / 200) loss: 1.413817\n",
      "(Iteration 191 / 200) loss: 1.428670\n",
      "(Epoch 5 / 5) train acc: 0.530000; val_acc: 0.366000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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u1q9fn9B/Xl6eqZhwu90xSS2MxEmy8Xcn2XL39/v9DB06lMLCQt5//322bNlCS0sLGzdu5OjRowQCAX7/+9/HtB89ejQATzzxhKO+s03JksWo/NhzpvLzKVmyuFvHsXfvXiZOnMitt97KZZddFnEt/uijj3jjjTcA+O1vf8ucOXMS9vV6vQwdOpTXXnsNgF/96lcRax10fVdt2rQJr9eL1+vN9nQEQegliKDLINmI/xGE/sSi6aNZefk0RhcXoIDRxQWsvHwai6aPTvuYu3btiiQpuO+++7jvvvt45JFHuPTSS5kzZw6f+MQnIguje+65h1dffZUZM2awfv16xo0bl6GZ2aT8Kqh6GLxjARX6v+rh0PY0ybWwjT7mxo0bGTFiBEOGDLG071lnncXhw4cjC+JAIBDj/mfE4MGDOXHiROT1+PHj2b59OwDbt29n37596UyjW8iWu/+ll15KMBikvLycu+++m1mzZlFaWorP5+Mzn/kMl1xyCTNmzIi09/l8fPnLX+Yf//EfGTFihNNpZRVvVRWl967AXVYGSuEuK6P03hV4q6q6dRxPPfUUU6dO5dxzz+X999+PuBZPnjyZJ554gvLycj7++GP++Z//2XD/J554gqVLl1JeXs7OnTsjsXkAQ4cO5cILL+Tmm2/m8ccf75b5CILQO3DnegB9iWzE/whCf2PR9NGOBFw8CxcuZOHChTHbTp48yfvvv4/WmltuuYWZM2cCMHz48BjLz+rVqzM2DtuUX+VIwMWza9culi5dGrGC/eQnP6GhoYFLL72UESNGcMEFF0Ta3nPPPXz1q19lxowZzJ07NyPC1ufzccMNN1BeXk5hYaEti8+AAQNYu3Ytt956K36/n2AwyOLFi5kyZYrpPhdffDGrVq3i3HPP5c477+SKK67gl7/8Jeeeey7nn38+Z555puM5ZZPKiZUZj9ceOHAgL774YsL2efPmccMNNyRs/+IXv8gXv/jFhO0+ny/m9cmTJ03f6068VVUZFXDjx4/nnXe6Ki0lm2f4vTvvvJM777wz5r3jx4/jcrl49NFHiSfepffcc89ly5YthuO54oorWLlypZ0pCILQT1A5S8ttwsyZM/XWrVtzPYy0mL1qAwcNxNvo4gI2L5ufgxEJQu7ZvXs3kydPzvUwYli9ejVPPPEEbW1tTJ8+nZ/97GcUFhbmeljdzsmTJxk0aFBE2J5xxhksWbIk18MShD7Fhx9+yBe+8IUYcWiXefPm8eMf/zjy8MmMnvh9KwhCeiiltmmtk3/ow21F0GWOcAxdtNtlgSfPscuYIPRmZIHRcxFhKwh9C/m+FYS+gx1BJy6XNnl2x0EefOkD6ptaKCsuYOnCsyJiLfy/2fuCIAg9iSVLlli2yB09epQFCxYkbH/llVcYPnx4pocmCIIgCIJFRNDZIN4CF85iCcSIOhFwghCL1lpSbPdyhg8fzs6dO3M9DEEQTOhpHleCIHQfkuXSBpLFUhDsk5+fz9GjR2WxIQiCkCW01hw9epT8uNINgiD0D8RCZwPJYikI9hkzZgwHDhzg8OHDuR6KIAhCnyU/P58xY8bkehiCIOQAEXSpqFsDr6wA/wHeyB/B/W1f5vmO2IKgZcUFJjsLguDxeJgwYUKuhyEIgiAIgtAnEUGXjLo1+Gtuo3FHPsHmUbgL27m7/JdQRkTUXTngdVaop8F3CLxjYMHyjNaOEgRBEARBEARBMENi6JLgf9RHw5ZCgs1uQBFsdnP0rUHcVr8WBfzToDdZ5fkPClsaAA3+/bDu1pBVTxAEQRAEQRAEIcuIoEtC45YAuj32FOl2F211sG9VJb6ip3G3t8buFGgJuWgKgiAIgiAIgiBkGRF0SQhZ5hIJNLspf6KcisHt1BYZFOH1H8jyyARBEARBEARBEETQJcU9wmu4/cgQ0GgaPG58I4YlijqvZJkSBEEQBEEQBCH7iKBLQskdd6EGeGK2tbrhN/O6CiS3ulzUDC3uauApCCVGEQRBEARBEARByDJpCzql1Fil1H8rpXYrpd5VSlUnaXu+UqpdKXVluv3lAm9VFaX33Ye7rAyU4vAQ+OnnFZun5MW0O+TOAxR4x0LVw5LlUhAEQRAEQRCEbsFJ2YIgcJvWertSajCwTSn1J631e9GNlFJ5wAPASw76yhneqiq8VVUAVK+toOFUQ0KbUYPKwPdOdw9NEARBEARBEIR+TtqCTmvdADR0/n1CKbUbGA28F9f0u8DTwPnp9pVLnt1xkAdf+oD6phZGjLoEz7A1BPTpyPv5eflUzzA1TgqCIAiCIAiCIGSNjMTQKaXGA9OBv8RtHw18CXg0xf43KqW2KqW2Hj58OBNDygjP7jjInc/s4mBTCxo4fGgKrQ2X4/WUoFCUFpXiu9BH5cTKXA9VEARBEARBEIR+iBOXSwCUUoMIWeAWa62Px739EHCH1rpdKZW4cyda68eAxwBmzpypnY4pUzz40ge0BNpjtl1QF+Sba1oY0RzEXdpOyZIOmBjVoG5NqA6d/wB4x/DWJ7/L4vfOoL6phbLiApYuPItF00d370QEQRAEQRAEQeiTOBJ0SikPITH3pNb6GYMmM4HfdYq5EcDnlVJBrfWzTvrtLuqbWmJez9u/jf+zcw2e9pDIC9bX03DXXUAo1o66NbDu1lBxcQD/fqZu+z7nBb7FQeZwsKmFO5/ZBWBJ1EW7e4oYFARBEARBEAQhHqV1egYxFVJpTwAfa60XW2j/C+C/tNZrk7WbOXOm3rp1a1pjyjSzV23gYJSoW/PS3QxuaUlod6qokC9/dgVv5FczikSX0aB24aKDej2CVzrOZaH7bUZxJFSvbsFyw6yYYXfPaAthgSePlZdPE1EnCIIgCIIgCH0YpdQ2rfVMK22dxNDNBr4OzFdK7ez893ml1M1KqZsdHLfHsHThWRR4ukoUDG5pNmxXdOoUGijRh6ktKqRiTBnl48dSMaaM2qJC3KoDl4IxriNcl/dyp+jT4N8fsujVrUk4ppG7Z0ugnQdf+iCTUxQEQRAEQRAEoRfjJMvlJsA8MC6x/T+l21euCFvCwm6P7sJ2gs2Jp+zjIYpBk5Yxp30MrXmKQGe8YIPHjW/EMAAqT4XEYEIoYaAlFHMXZ6WLd/dMtV0QBEEQBEEQhP5HRrJc9mUWTR/N5mXz2beqkpJZHlReR8z7p93wq3kKpeCE2xURc2FaXS5qhhYn78S/H1ZPjbHUlRUXGDY1226XZ3ccZPaqDUxYVsvsVRt4dsfBjBxXEARBEARBEITuQwSdDbw3+yid1Yy7MAhoPh4Cj35esXlKXtL9DrmTvw8kuF/Gu3tCKIZu6cKz0h1+hPhyDOFkLSLqBEEQBEEQBKF3kXZSlGzRk5KiGBJVlqB8/Bh0knIMYUoDQdYfqLd2fO9YWPIOkJjl8uJJI/nv9w87znoZn+wlzOjiAjYvm2/7eIIgCIIgCIIgZA47SVFE0DmgYm0FDacakrbJVx58J9qoPByqS8cZFbBnPdq/3zAAUaNQvqaE7UZZLz0uxaB8N03NAVsCb8KyWoyuugL2rZIi6YIgCIIgCIKQS+wIOseFxfsz1TOq8b3uo7W9NbLNrdwMGjAI/2k/o4pGUT2jmsqJiSLp775PGZY4+DsjGGXQl1HWy0CH5lhzAMBWjbuy4gJDC12m4vMEQRAEQRAEQegeJIbOAZUTK/Fd6KO0qBSForSolB/O+SGvXf0addfXsf7K9YZiDmBl25dp1gNitjXrAaxs+7JheyvZLa2WNchmfJ4gCIIgCIIgCN2HWOgcMufdDs58pJ1gQxB3aTslSzpgYur9tg75LMuOw+3uNZSpo9Tr4fwoeBXbhnzWsL2ZVS0eK8IvvhyDk3g8QRAEQRAEQRByhwg6B/jXraPh7uXo1pDLZbC+noa7lwPgrapKuu/ShWdx5zNtPN82J7KtwJPHShMrWaj9rgS3y3isuk0umj5aBJwgCIIgCIIg9HLE5dIBjasfioi5MLq1lcbVD6Xcd9H00ay8fBqjiwtQhDJMXnHeaB586QPD2nDx7YsLPHjyYtOqiNukIAiCIAiCIPQvJMulA3ZPPhuMzp9STN79nq1jGWWxLPDksfLyaaaWtPiyBuI2KQiCIAiCIAi9H8ly2U24S0sJ1ifWl3OXlto+llEWy3CSEzORJm6TgiAIgiAIgtC/EZdLB5QsWYzKz4/ZpvLzKVmy2PaxzJKZWElyEubZHQeZvWqDocumIAiCIAiCIAh9D7HQOSCc+KRx9UMEGxpwl5ZSsmRx0oQoZm6STmvDxbts2qlL11cRl1RBEARBEAShryMxdFmmdm8tNdtrOHTqEEM8I/l4/wKaj50TeT8cJwfYjqGLZvaqDaZlDUanIWZ6uxhKJyaxP9Dbr6sgCIIgCEJ/wE4MnbhcZpHavbX4XvfRcKoBjcYfaMRVshb3kB2RNtFxcvFZL+2Ij2SumWFrnVUXzLAYOtjUgk5j/55AspjE/kpfuK6CIAiCIAhCLOJymUVqttfQ2h5b1kC5Agwc+RLB49Mj28JizOPdSdGnahh86hBFRaPweKsBa4IuVeHxVAlWoi03LqVoj7Pcpto/GbmwCmUiJrGvkU7iHUEQBEEQBKFnIxa6LHLo1CHD7crTFPO6rLggwZrXcKoB3+s+avfWWupr6cKzKPDkJW1jJmbiLTfxYi7MwaYW28lWUlmF3nr+pxzyfYqOe7wc8n2Kt57/qeVjJ8Ms9tBqTGJfRESuIAiCIAhC30MEXRYZVTTKcLsOFEf+DhcDN7Lmtba3UrO9xlJf0S6bZriUMsyAaWS5McOum14yq9Bbz/+Uqdu+zygO41IwisNM3fb9jIg6I4Hb3wuvi8gVBEEQBEHoe4jLZRapnlGN73VfjFDzqIHknaqiGWLcD5fXGVvzzKx8RizK28yigSvQ+Qc4posAKOYk9XoE/6fgQt4d8SHK00RToJjvrf8ccD2Lpo+2baGJd9NL5lKZzCo0dvuDFKi2mO0Fqo2x2x+Ey26yNaZ4wv1LApAuli48yzBRTH8WuYIgCIIgCL2dtAWdUmos8EtgFNABPKa1rolr8zXgjs6XJ4F/1lq/nW6fvY3KiZUAkSyXo4pGUT2jOrI9mlFFo2g41WC43RJ1a2DdrRBoQQHD1MnIW28PambfiO24XCGDrBrQhC5Zy31/drNo+u2m8Xd5BrF0YcJCLVW5hGTlGEpaDoNKPHaJPmJtzimQwuuxiMgVBEEQBEHoezix0AWB27TW25VSg4FtSqk/aa3fi2qzD5irtT6mlPoc8BjwaQd99joqJ1bGCLiNj69g81fvoNjfzsdeF88uKORPZ51miCsfj9YEVJfCye/QVI+weLpeWQEBY2tYzdBiWl2x3rXKFaC5aB1wu6nlZuXl03jwpQ+S1sdLlWgjmVWo8bmRjOJwwrEb1QgsyljBJiJyBUEQBEEQ+hZpx9BprRu01ts7/z4B7CYuJaPW+nWt9bHOl1uAMen21xfY+PgKih/6LcP87biAEf4Orn3+JBe+G8Tf0YLWmuL2dpTWlAaC+I4cpXLHH6wd3H/A9K1DbuNkKa7O5CzJSiakikVLlWgj2bH3z1hKix4Qs1+LHsD+GUutzFgQBEEQBEEQ+j0ZiaFTSo0HpgN/SdLsm8CLJvvfCNwIMG7cuEwMqUfieWwNAwOx2/KDcM1GzeYpEHS5KAgEee2j/VEtzOPb/OvW0bj6IYINDbiLSimZegzv+MT2o4LtNHgSL7V3QEnkbzPLTSo3vWQulamOff5lN/EWMHb7g5ToIzSqEew/bynnO4yf6w6kQLcgCIIgCILQE1DaJEbK8gGUGgT8GbhPa/2MSZuLgUeAOVrro8mON3PmTL1161ZHY+qpvDtpsqFJtAO4+s6Q4FJaU/dhlKBTeaA7wDsGFiyH8quAkJhruOsudFsgqqmm9PymBFFXW1SIb8RwWl1d7pweNZB75/zAMJ7PDs/uOMj31j+BGvYiytOEDhSjP/4c91dc32cFTnzcIHS5qFqdswhCQRAEQRAEwQyl1Dat9UwrbR2VLVBKeYCngSeTiLly4D+AL6YSc32dJq+x6+PRIV1/jwrGlQ/Q7YAG//5Q0pO6NQA0PnBfjJgLNVU07vICCgqGhf6hqHQPxzfhS5QWlaL0qfPaAAAgAElEQVRQlBaVphZzdWtg9VTwFYf+7+w3Ho93J/mlz+Aa0IRS4BrQRH7pM3i8O1Ocjd5LsrhBK6SqzdedPLvjILNXbTAsZyEIgiAIgiD0fJxkuVTA48BurfW/mbQZBzwDfF1r/T/p9tVXCNx4Facf+m2M22WrG34zL2Q5y1ceqk9rQIFydYq56AO0hJKflF9F8IjfsI/gKRf4mhK2V3b+s0RUxkygS0xCxEIYpmZ7DQF9OnaY+jQ122tMBaMT61SqfbvD8uW0QHeqRDLdRaoMpYIgCIIgCELPx0kM3Wzg68AupVTYHPM9YByA1vpRYDkwHHgkpP8IWjUd9kXmfXM5GwnF0kVnuXz9rNOUxpc08BUbH6Qz+Ym7MEiwOfHyuQuDzgdqlDEzSkxGY1Ynr+FkAxOW1SaIqlQiIpkgs7JvdwgUK3GDyXAqCDNFTxGWgiAIgiAIQvqkLei01pswrCIW0+ZbwLfS7aMvMu+by+GbyyOv/9GsoXdMyDJmtB0omeWh4c/t6PYur1mV10HJLI/zQZplzDTYblY/ryNQHONOCCFRlcpdMZkgSyVAukugOC3Q7VQQZgqnwlLiAAVBEARBEHKPoxg6IRH/unXsmb+A3ZPPZs/8BfjXrUvvQAuWgyduge8pCG0HvDf7KJ3V3GmR07gLg5TOasZ7s8/S4Wv31lKxtoLyJ8qpWFtB7d7arje9ZtUlNKyeylvP/zQSd3XswCV41MDYVh0eTh9eGHkdLdjqm1q4zLWJTQNuZe/Aa9g04FYuc22ivqklpdhLJUAybfkyiy9LVorBCqlKQVgdh1PMBKQVYdmT4gAFQRAEQRD6M46zXGaa3pzl0r9uHQ13L0e3tka2qfx8Su9dgbeqyv4B69aE3Bz9BxKyXBq9Xzv9S9Qc+QuHTh1iVLwLZxS1e2vxve6jtb1rnPl5+fgu9IXax8fQxdGiB3BH4Fs83zEHgMKhbzNs7CscDxymvc3L6cMLCR6fHrOPAvatqsT3w3u4PfAIhaot8l6zHsCPPN/miZMXYHQ3hvedvWqDoWUrTyk6tMalFO0G9/Po4gI2L5tvOBczjDJZelyKQflumpoDji1SVq1bmciomWwM6R7b7Fqkc64FQRAEQRCEWOxkuRRBl0H2zF9AsL4+Ybu7rIwzNryS1b5TirQoKtZWGLpJlhaVsv7K9aEXEbFo4PYJHOgYwZy2hyOvwwv5VAv95gcmUdiS2HdzQSmf1Y8k3ddIgKQiXfFjNo90j52ue6JT4ZStJDITltUmFd+CIPQ9xM1aEASh+7Aj6DJSWFwIEWxIFCrJtqcipnB4aSklV8zC2/acocWuZntNjJgDaG1vNcw2aZbIJGZ7+VWhf75iMFi6l6nYChRht8ZU8WWFLcZ9F7YcYukXE/f1uBTNbcFIgpUrzhvNf79/mPqmFlOLXNhiZ5RUJXoxcvGkkZFjxbe14qZpNT7PSbIWJ26kVvo1K/qeip4SByiYI4tvIZNIVlxBEISeiwi6DOIuLTW20JWW2j5WvPtmsL6ehkeehvOb8I7XCaUELIm0TswSmYwqGpV4AJPkLPV6eMzr8EI+/MNuupBMkuwlfl9vgYdTbUGONYfqPBxsauHpbQcjVrEJy2q5zLWJ291rKFNHqNcj+FHwKtZ1zEmwEhktRn695aPI+/GLEzPBknAeLLRxkqzFiXDKZpIYp4lhnGJHnPdHZPEtZBrJiisIgtBzkaQoGaRkyWJUfn7MNpWfT8mSxYbtkyVQaVz9UEwsHnQWDq8bHHldO0BRsXUF5U+U01kWIgEjkVY9o5r8vNhx5uflUz2jOvEABslZWvQAfhTsiuWLX8gvmj6azcvms29VJZuXzY/9sU+R7CV636KBbgLtsRa46CQp1w96k1We/2CM6wguBWNcR1jl+Q+uH/RmwjSMFiPxRB/bKHGJEWFhlSxxiRMrm90EKlaOn4nyCE4TwzjBKCHLr7d8FPN66e/fZvqK9f22YHqqBEO5RIrZ9056SrkVQRAEIRGx0GWQcOKTaDfJQXMvonH1Q9TffkfIbXLJYrxVVcYWuLuXR45j6r7ZnMfu35USKOrgpYvzaChRgMYoFtJMpIVdMGu213QlUBnxaSqfuwP8X4t15wwnYYlKvvLOJ7/LtvfOQKVjDTE4XkKyl05SLSBu9zxFYbAt5r1C1cbtnqeAH1g6ltmxzayF0QIzLKxSWUOcWNlSWjyTkE6/dtz00nXXdIoVcR7o0DGW3f5mneqpi2+xHPZexM1aEASh5yKCLsN4q6oiwi6ZaDO0wLW20rj6IbxVVabum+HSf55TedzwR2hztbN5SpcFx6VcaK2TZrmEkKiLvBef1TLOnTNG2AHnA5svMz8HCbF/nSI2QtzxzEi1gEgWj2f1WGbHhkTBYiZ2Zq/akNQVyal7YrrCyW6/vWWxnY4o6W+uYT118S1ue72XXLtZC7FIjKwAch8IXYigyyLJRJupBa6+nt2Tz0Z5vSiPBx0ImB4/PwjXbNRsnhJ1fK2pu77O3kBfWZFYoiDQEtpuQXhFk8rymIravbURy+GQcSMpVAtoPnZO5P2YBUSK4uvRGC1G4km1ODETVqmsIU6sbE6w26+VxXayH49sZdSMx6o4jyfX1qnupKcuvnuq5VBITa6+x4REesvDNyG7yH0gRCOCLosky3ppboEDtEY3NYHbTV5xMe1+P5iUlxh+PPa1YWITUljN/AeMx2G2PQmpLI8QK9qiLYnxpRf8gUbyS5+haKCbI4emJC4gFixPrJcXFY8XjdFiJD6RxsWTRvLgSx+w5KmdGXdtzJV7op1+Uy22k/14AEl/WDL5w2NFnBuRa+tUd9JTF992LYfy9LlnkavvMSEWsXQLIPeBEIsIuiySLOtlyZLFCUXIEwgGUYWFTN7yhmmNu6NDuv42i5kztJrddRe8eAfeknpQLtAGi2MDS1cqzERsoL6e8l9MZYiG5jw3gc7+Gk414HvdBxiXXgjo04wY8zJvLb498aA24vEg+WLEieAwEhjx5RZ6w0I01WI7VaKNZD8smfzhSSXOk8U79id64uLbjuVQnj4LgjFi6RZA7gMhFhF0WcRItIWzXsYnUDGzwIUFktGxOgZ6eLFiMIoTSWPmDK1mbQEatwTxXqaNxZyJpSsVZiL2yBDQSuFXJPQXrpdnp/RCBIvxeKlwIjislFtIthBNaYWIFHkPida3PvldFr93RsatFqkW2+n8eITfM2tzsKklLdGbSqyIZadnYsdyKE+fBcGYnhojK3Qvch8I0YigyyJGWS+jxVx0AhUzC1y4hp3ZsVZWVbEyxTiSZcwMU1tUSM2wYg7l5TGqA6onfonKNISSkfBsdcNv5hmXVQgTdr+0XB8vwzh90hUtMGav2kBTS2zso9lCNKUVwiBhzdRt3+e8wLc4yJyMWi1SLbZT/Xgkey9Z3Fu41EAmrS890TolhLB6beTpsyAY01NjZIXuRe4DIRoRdFkmWrQlI5k1z+6xIDZmDpcL2hOtcO7C0LbaokJ8I4bR6gqVJWzIA9+BP8LeWaZZMs2IF56HB2t+M0/FZOI0ImxhjI6hgyT18TKM0ydd0XGB7cO9uDsWEjw+PaaN0UI0pRXCIGFNgWrjdvcanm+bk9jeIckW26l+PJK9ZyXuTawvQjTy9FkQjOmpMbJC9yL3gRCNCLoegpEF7uDX5nLr6X/n0BN3pSxDEE18zJyRmFN5HZSUnwCgZmhxRMyFCbtB2hV04bmE51P9n1NpyEtunQuLNsP6eBbn7BQnT7rik7m4BjSRX/oMrRAj6owWoimtECaJacrUUUvHySRWfjzM3ovf19jBODYBS3//kerv50CePguCOeKFIIDcB0IXyqggdS6ZOXOm3rp1a66HkXPiRQKEhI/vQl9KgWPmvonSoEOWuZLyE3jHhxbP5ePHcuF7HVyzUTP8eCjRym/mKV6f4rZfAiF+HhvvxrfvD7S6ukSdu0MzyFOIv721W0VbKuzGsoUTsFSsrTB0Fe1oK+bU/y4DQgvRlZdPS/jinb1qg6EVYnRxAZuXzYfVUw1LMxzoGMGctocjr/OUokPrXrPwTzZvs4W80fnrq8S74kIo0c6gfDdNzYFec52d0t9FrSAIgtB/UUpt01rPtNRWBF3PxEwkWCkcvnvy2SZJVjSTr446psoD3cGdJ8u46o+a/GDXW61uWLNoGCt/uDnxMCbCxozajXdTs/cPHHLRFZ83795k0+95xMeyQShxTNXDlO/4IdrI5qTh5Purki5EjRbuMeLFoN8WPYA7At/i+Y45hkPtDeLHaN6KUDxdnlK0G9y/EZHbi0hXkJgJ3mjsXOfeIox6yzgF58i1FjKN3FNCX8OOoBOXyx6KWWbHDt0BxKb7jxd1puUSCuNcL3UH+Jr46kWfwRNsinkrPwhffbUjcQAGSTpYd2vobxNRN+fEBZz569ejkrlcYNiuR5Ok+PqosWWG4rt0UCnrVyW3PKZ0YzQozfDOJ7/LtvfOQDW14DIQP70hFi163gebWiJiDjAUc9D7kmHYTbsfvRix8pjN6nXuLen/e8s4BefItRYyjdxTQn8nbQudUmos8EtgFNABPKa1rolro4Aa4PNAM/BPWuvtyY4rFroQZha6eEqLSll/5XogKhFKfT0oFWOlU3kdlJ7vj7hZAuAdC0veMbfoKcXk3e/FFiUv0pRMPRZ7nFAHIYEYZ7FLiOcjlOyl9N4VlhO89Ah8xWC4zFbUXvdk2u6xTpmwrNZkVLAvhZjsKVixRkH3WeicPOWN3tdIbIPxPIwsllawcp1TuvVmmExbJe2MU57Q9w66+54U+j5yTwl9ETsWOlfqJqYEgdu01pOBWcAtSqmz49p8Djij89+NwE8c9NevqJ5RTX5efsp2YUteWDhFLHM65ASogY+9Lg5dFIgVYVF15sKlEeJxl5bGHldrgieh4S0v/g/jEnzo9lBvYYtd3RrApAZeayuNqx8y7NO/bh175i9g9+Sz2TN/Af5161Keg7SpWxOKUfMVh/7vHLMhZkXWvWOonFiJ70IfpUWlKBSlRaUJYq52by0Vaysof6KcirUV1O6tzcgUzDL+9aZMgFYsb92VDCMsrA52WsnCT3mf3XHQ9r52LI1G2U6tYOU6p6oBOHvVBkvzs4KT8+e0TIGTvoXuRUpSCJlG7imhv5O2oNNaN4StbVrrE8BuIP5R6BeBX+oQW4BipZSxehBiiBcJLmV8qcI12oyEkyJU0Pvmb7tY+pliakeODW31joWqhyNWtJIli1H5seIxXDLBUJC1u2isG2w++E5XREhSA89ge4J4rK+n4a678H/7bGuiyw5h11H/foyEaAILllM7pJiKMWWUjx9LxZgyaocUR0Rx5cRK1l+5nrrr61h/5foEMed73UfDqQY0OuIumwlRt3ThWRR4YktC9LZMgGaiJE8pFKEnrJmMCXx2x0Fmr9pgKGbMykjctubtlOLHqiizk+0UQp/j4gIPnrhssVavczLRl2nhk6wMRyqsPJxI59pZ6VswJtn5dkJfeBAl9CzknhL6O04sdBGUUuOB6cBf4t4aDUSn6DtAougTTIgWCffPuT/BYhddo81MOA0/Hvq/VQeo+UQZ+JpgyTsx8W7eqipK712Bu6wMlMJdVhZxibRSlNyQznT7yax/8RiKx7YAjVsCWBJddkgSE2dE7aAifCOG0+Bxo5WiwePGN2I4tYOKUnZVs70mxh0TuspCOGXR9NGsvHwao4sLIuLnivNG8+BLH2R8EZYtzETpv151DvtWVUbcZTKxsExlxTETVu2dFu+DTS0s/f3bTF+xPmEsTiyNZouO0cUF7FtVyc57KnjwynNirnO8yDVbfBud33icCJ/ofs1cZ62cm1QPJ9K9drl4Qp8tIdSdZNPi2RceRAk9C7mnhP6O46QoSqlBwNPAYq318fi3DXZJ8ENSSt1IyCWTcePGOR1SnyRVjTazRChHh3T9bZZoBcyLlpsmWBmkAAXK1eluGX/AkIuilYLpYSyJx7DoSpJV0xIm9d3Mttdsr6FVB2K2teqApVp9ZufdbLvdOKDoOjSZDgzvjpikVIlhMjmnVIXczYpZRxPo0BxrDiSMxWxfKyUlrNRcS1ZvyMo5sloD0A5WY/+sPCVPdR+ke+2sPqHP1L3eV5IzpDrfTpCCyEKmkXtK6O84EnRKKQ8hMfek1voZgyYHgLFRr8cACepAa/0Y8BiEkqI4GVNfpnJipal4MBJOre5QPbkwYfdMO5gKsntWQFWVeTr/TldEo4LpJUsW2xOP8dk5zcSYHbxjDOu7mcXK2RVl0YwqGmWY4MboejhdDGZyEdadC9NkYsXpnKxkjwyLGSNhlYrwWJzUz3O6GEl1jqLPr1nygHRck6y4mdp5Sp7sPkhlgXNSiLw7Hxr0FrJt8ZSCyP2D+AclF08ayX+/fzgrokvuKaE/k7ag68xg+TiwW2v9bybNnge+o5T6HfBpwK+1Tp26UbBNtHAKNNRzdIjiybmweUrIuhXtnpnuccOCbNDci2hc/RD1t98REmhXfBNv23NddenOqAhZ0Z65Ebxj8C5YjnfDKyn7MhSPeR2UlJ+IG5RJgpIU1O6t7bJwfqKYan2CyuNR5RqihGg8dkRZPNUzqg2zYBpdD6eLwUwuwrK9MI25HknqKjqZk10LUrywMstUaTQWp6LM7mLEjlCNxorwsWqtShX7l8kFWyoLnJPzn8l7vSe5fjrBqcXTCZKttG9g9KDk11s+irzfW63XgtATcWKhmw18HdillNrZue17wDgArfWjwAuEShb8lVDZghsc9CekINptsnZvLXu316BSLJbtHje+DEGwvp6Gn70A964MtUmjTl10PxAlHocPoeSsg3jHGlv/UhJVAL125Bh8g/JopbOOX8CPb3gxDBxM5eHUBdINRZnWVO97J5SsJcm+qdxlo3G6GMzkIiybC9Nwopjw+UxWV9HJnKxYkDwuRXNbkAnLaiOLx3DcXjqCsDsWJk5cHTPp4mp2bbKRKjwdt9RwLFu6wjSdez2XQiiTOLF4OqGvuKwK1r5/e6P1WhB6ImkLOq31Joxj5KLbaOCWdPsQ0ieZe6YVYmrPRblJJitD4K2qgldWUDtAUfOJMg658xgVbKf6WBOVFuPevP/Qgrfq7+CvB68Lzvgy7FnfZf1LIpxiiBOWNQPbaY27XVvpoGZoMZW3vJPycLGirCE0r4+PUXmqGTiVUrRavR5OF4OZXIRlMyYpWaKY+PPkZE6pLEjeAg+n2oKGMXHR4iA8j3D7QHuXTSwXgfdOXR2TCR87xeq7c9Fv1wKXCWGajgjLlRDKNLmKSeorLquC9Qcivc16LQg9EcdJUYS+h6EV7u6QVSxVGYLa4Mf4Rgyj1RVKoBrKBjkMjnxMSjljZN17+zcxJRYsE5fF8pDbOMvfobYmw+1GRETZ6qngj8v0lqFkLU4Xg5lchGUzJslOTKKTOaWyIM1etYGmlthkN/GLRyPxk2t3sEy6OsZfKzv187p70W/HAmpHGKRzr5vdB30pOUMuYpKsWEt7wmdQSI2VJFPhdoIgOEMEXT/FzAIHyYuBmyUuweVi9+SzKR7s5bx5ms1Tut5qdbmoGT4staBLVkrArlCKS5wyKthOgyfxdh8VtF/M2W6GTDtkYjGYqUVYNmOS7MYkpjunVAv1dFztekLgfSZdHZ3Uz4OecT6MsHNtM239cxIP2d8FSiprqVOXTKfnWq5VcqLPj7ezfma0R0M8vdF6LQg9ERF0/ZBkFjhvVRWBhnpDX9q2+noev7yI656HgcGu7RpQ7aEf12HH4aYXNNAeScgCcCgvqXdu58AyKJTislhWH2uKsRwC5Hd0UH26a4xWk3TYzZBpl5SLwajYQFtuqBkYi3/dOvbcdl3KjKWpFtN2EsU4HT+YL9Rzneo+3f0z6dbnpH5erkl2/uxe22xZ/6zMwY5AcXLP9QYxkureTufch+d9sKkFRVftpHTEYH+I70v3Pok/P00tATwuxdBCD03NgaxnuexOesNnyYjeOm4hNSLo+iGp4uCODcljmD/xif3RIfCns07T/HnFNRs1w4+DVpAX9/AtPwjXbIy10o0q6iokbmodzIRQioid/RD10x2KdYOaocVdsX3Hm6m85EHAXpIOFixPWqohfDxL4tAuDpLOOCXVg4BoUi2m7SSKcUqyhXouU9072T+Tbn1O6uflklTnL5uxbHYtu8kWUXYEipN7preIkVT3djrnPnre8bYiO0K8P8T3OblPjM5PoENTOMDNjuUV2RlwNxFveYyOpe6pn6V4est3gJAeIuj6Iani4H49V3PjCyFhFia6pt3mKXkRsfa7lcH4wwAwPKrEfLTlJakoMBBK/v1DaHx5EMHHzu4skTArtkRCtHUqoSaeJiLqvGOpPKOCyugEK5c8GNnXTpKOSH8mVjJb4tAumXRLtUnKhDhRLF14Fi+s/jnX7KplZEsThwuK+c20Sj7/la5Et04T92SCXKa6d7p/plwd06mf1xOe8lqpvRduZ5bNM905mIlgTajOn52soXYEipV7xmxevUmMJLu37VperbgUO03ekY6Q76k4uU/6SsmOeIwsj/H01M9SNL3pO0Cwjwi6fohpAe/SkBXtfy8YzU85ELHCHR0SEnPRLpRhjg6BkccTNtPkzUOhEiwvZqKgfuntNJaVxdS08zeWceAtF642PxASfx/95Gl+U6GoLR8TsrK9vDQUm1d+lbHY6RRzLEmeydJ24fDyq2IFVN2azmQpB6gZN4bWOBdTU3FolyzG76Ui1YOAaC4+sJ0zdq7F1XYagE+0NFG9cy1jDpwDPeyHI11h5HTx0lMWP9nMHplNrJw/s2vrdA7JCtDHHyvVIsqOQEk152Tz6in3m1PsWl6tzM+qi7Wda9VTPid2cXKf9LSSHZkS1FbjjDP1WcrWg4C+8h0gGONK3UToa5QsWYzKz4/ZpvLzKVmyGAjFN20rL+KWW9xcfaebW25xG4o5CAm9057YbSo/n6nfX0nd9XWsv3J9jIgxEwUQEmwHHluH/1MrwdfE397x4mqL/RJ1BxWf2wRaqVAGzaGDqH1tRehNB2LHLBmHlcLhEcugfz+gOWTyqTIVh3Ywcz812e5ft4498xewe/LZ7Jm/AP+6dWl3HRb8VrY3rn4oIubCuNpO07j6obT772mYLVLsLA6d7J9JFk0fzeZl89m3qpLNy+YnXTwkEyjpEC6ZMGFZLbNXbeDZHQdT74Sz8+d0Doumj2bl5dMYbdJX9LFSLaKWLjyLAk/s96uZQDGbm0spJiyr5bY1b5vOKxv3W7rXzgnR514RSgSUzJqcan523HDtXKtMf066Cyf3iZ3zk23CgvpgUwuakKBe+vu3mb5ive371U4dWKcYjfvOZ3Zl5LPVk35zhMwjgq4f4q2qovTeFbjLykAp3GVllN67IuI2VzmxEt+FPkqLTBbwyk3xwGIUir0XjKFp8VdNj5Wwr4koCOM6HeBvD94fattoXFIg2p2z1eXie0VQ/kQ5FePGUFtUaDDh1DF41TOqyc+LFbmWk3TEWQbNMmdGi8PavbVUrK0IjXttBbV7a1P3AyHXTk/cl69JsfWwe2uwvh60jri3pivqUj0IiMaONa+34nTx0pMWP3bI5FNeJ4sXJ+fPamr8ZGIlLILN0j2Fj5VqEWVHoBjNGUKlJjTJS05k+n7L5sIzFfEPIADTa2U07/A1SyUGjfqNv1ZXnBeywsb3nc7nJBcCOR4n94ldsZ1NzOL5jjUHbN+vVgRPpr67s/kgoLf+5gjWEJfLfoq3qspUdEFsfJOlBB/ftNZvyZLFMTF0RoSF3BETd86jQ2Jfd6hQnFxDnsI3Ynho/KeaqS0qpGbYUA65FaPWViRNuuEoSUecBdAwo2aUOHQUY5cifi8aOzFvVvBWVcFHW2j8+TMET2rcgxQlN3ze8Fip3Hr7Ak4Tk/TWemWZdKtyEtPh5PxlMjV+qmNZcRG06vYbP2ejIvBGlBUXZPx+6ynxOFZKSITHaxZLOXvVBsvnJPqYyfq2+znpKS6aVu6TZC6B3VnGJNk4rDxgsnq/Gn2GPS7FoHx3JHtnb3CL7K2/OYI1RNAJKclk8oroWneBeuPyCEc6BduLFcO46tmPTZOzGNHqUtQMHw4oXmos5vu/1ww/HuTokP2snX8X3GgumipPnqJyf32nUOqAM09ZnFRsds5IRs3hwzmU50oQh7YSsBgRH79nQsatZHVr8DY9jvcLUT8sTY9D3TkJ4zES7mbWPLO+uqs0gxOcLl66c/GTKbqjZILVxUu65y+TqfFTHSvTi6joOU9YltqyHz+WTN1vPSVBiJVrla1YymR92/2c9BSBDMnvk54iPFONw2pRcyvfNU4/w3bu/WzHIfbG3xzBGiLohG4nbB288/uzDQXbixXDuAiY843v8fPgXVy54XTK5CzRHMpzsck/gRte7Dr2yOPwzedO07b+dna3LE2soWZUDuDZb8OLd0DLseSiYsFy/DW30bgjn2BzHu7CduZMb6Wy2ri97QQscVgtiZBxK5mNDJvRwj1VzboEcliaQTAmfkFyxXmjM1JLqjuTKNiZQ6aLkjtZRKVTa687Sk70lAQhTh4KOBVRyfq2a+kys7MebGpJyJyaS7pTeDop95EscVE0Vr9r0v0M2733s1luRejbiKATcoaRYFs7fyALv/E9oNOSdiP8cGaXeLlozEWUHniVQ6cOoZSiQ3ckHHdU0Sg+t35/jFAEGKBhQHOofUINNSOx0hGAlo9DfycRFf6/FdDw1lB0WyiVcbDZTcNbQ+FvBXjLE+c9qmgUDacSrWRWErAYuWu+9NhdjH39h3gO+2OEk2MrWTw2k86kcus1JYelGYREjBYkT287mJHYmO5avNidQzaLkofHY+WJfbq19rojbinVtYueo5FrqNVyC6lw8lDAqYXYSvmKcIwfxLp3xtcyS0ZPypBpdm4ONtfBtrgAACAASURBVLUwYVltxh4iOC33ES+ojc53dwgluwJY3CKFdBFBJ+QMI8EWb21K5u4ZL26gK1ZtxPF/Sdl/TDyZlbT/UaIi2kr2k0c6GBaXjVO3BUxj1apnVJuOOxXx7pqz323nhheCeIKhjJJGxb7TspIZkYnC71awIBxNi9PnmJ46LicurNl8It9dixe7c8im0LTzxN5prb1wf3ZKUlhtm6zv+DkmS9Zi95zE4+RaObUQ2ylfYaWWWTJ6Sr2wZK6M0clGwJn4zES5j/gHLbmoC5jOQwNxixTSQQSdkFOcxOcZJTL5nn8Oo7/1b1j9qYzEk5mJlXj8BxKEZLHf2KUjWF8PvuKEBbSTBCzxbpnXbNQJlshooZq2lcwIg8LvZhk2HZFCOCYtTp9D8WRlXDkpNOzQhTXbtYu6Y/Fidw7ZFJp2xKWTWntgTyilI6rM+rZatyu8+M5VchyzZBfNbUFL1qbovo3ERfQcrJ6TZPSEemFWXBkzIT6tlPuwK+RzIZR6Wm0+oe8igk7o1cx5t4MzH2kn2BBEeU/AqbUEAwHTVOLxROLJjMSKEd4xCVYys+Lq7sIgoA0X0OkK2Xh3zeEG/UKWygPYyLDpiBTC0W72zu6ymqUaV86SCTh0Ye0LC5J05pCtxZ8dcen03NsRSpm0xFoRHtGL71wlxzFzyzvWHHokaEfUTlhWaxgHF56D1bmEaxv21M9c/Dkzcxh1Kj5T3fvdmajELtHH9hZ48OSpbnf1FPofIuiEXkX04lx5vXDqFDoQ+vHVTcZ16wBUcXFMW4iLJ4sXKwVDoe0ktLd1HaRTVBza8UNmv9vONRs1w4/DyXwI5IEnai2k8jooKT/RtSFDMWDx7ppmYhI0uydN6iwtcDneW+5Lr0MjV70l76Q9fsv9nHMN7FlvKBztZO/sTmteqnHlLIudzdjHeJy6H+bEKhlHT0o0YKVkQqYWg3aEUiYtsXaTtWT6oYFd19Hwe7NXbUhwhbT6GU01BytZF6OvbU+5X42IP2fZEJ+ZLPcRTzYfrhm51npciqGFnoyXOBCEaKSwuNBriC+UrZuaYgSaKUoxacsblN5/X9IC6P6/FbBn3SfY/VQZe16aSMPhS9nzX2Xs/l0pe/6rDH/xN6H8Kir3DOamFzQjj4c+QENaQXfAyUJX6NiFQbzjm2msGxza9/kS/B8WWF5AU7cGVk8NuWuunhp63Ul00XeF4sWKYXQM9MQdQIeCGVAET0LDI0/j/393Wes7fhzrbu10f4yyNEaNx+q47fZT+z9PU/GJYsonjKNibBm1g4oizc2ydBptT2Y1yzSpxpVt10VTzGIcLcY+LpqefrHgXBafjsbJHDJNsuK+8eerqSUAGoYWetIad6qi5um2TYXZHP/1qnMixcCj55DJgsdO7jknn9FUczB6P7zQj7+2mbhfu6tIebaKVWfzM5vN4t1mBc0LB7gN731ByBRioRN6DUaLcysERnqB5FkXjSw5TZGU/53C6GcvwLhZfPXVDjwGGTTVoCFM3v4G/m+fTcOfXej20POSUNZLLxQOwxvVn6EboIV4p3h3Tf+nu44VEnOxDqe6XdH482fw3nKfPfdDO656TuK04vqpLSrEN3QQrQE/kFh43U72zozX4ktCqnHlzHUxA7GP6T4J7y21tbp7HGDsKjZ71QbTxeCO5RW2+7JjmcykFdOuO1wmYxad3HNOPqOp5pDOOUn3fu1O9+5Mx5t2h0U/mw/XcvbgTuj3iKATeg3pLMJb3bDmIhcG1QNixA0uF7QnD1gPW3Y8h/2G74e3N9YNQbfHttHtLhrrhuDt7Lfhrru6yhzU19NwV8iC5v2r/XinaKG6e9IkMIggDJ7UIaG5yR3bbzL3Qzuuembi7w83wzM3Jo+3iztezdBiWl2xzgPRhdftZO/MeC2+JKQaV87c/ror9tEAWdwYY7ZYz/T5srPYzvTC3K4gyZTgdmplc/IZTTWH7nqo0N0PUjI1r+4Sotl4uBYWomYxhT0h/jGXpBLqPcE1v7fjSNAppf4T+ALQqLWeavC+F/g1MK6zrx9rrX/upE+h/2K2OI+mTUFrPgxq6SpE/voZJ1gZ1y7eIpdKzIUJL9STiYTgUeNMJeHtjQ/cFxFVYXRbgMYH7sN7if14p5gSCkMUwwwTtLTTuKUd3Rb7c5MsmYitMgVm49Od5zWZxS6un0PuxMLxoZjF/ey+++yIUDpjwyvGfdJ1Tj458xA3v6gYEOiat6NafCRPspLMCrxo+mjePraBp/f9jI68Y7jah3LFhP+ve360yq/KSQ2/vpBQJUx3LDiycb5SZcGMn1N03bSegp1zn00rW2+htz5I6S4haiW76cWTRvLf7x+2XO4jWebPXMY/9gShlEqo2xXyTufUE85JNnBqofsF8H+BX5q8fwvwnta6Sik1EvhAKfWk1rrNpL0gmGLk0obbTd6gQbT7/Xw8xMWv5naweUqsICg1KNidrvtmeAGfzLUupeA7YmzhCx7x2671Fl9C4VfzFDe/oBkY5RIaTtBSv6XYuN/6g5HyCrXTv0TNkb+Eyil8ophqfYLK41HJZsxc9ayUfTCzNMa5BI4KttPg6fpqmv1uOze9EC7PoFNaFqPPScMUF5p2vvZnxfDjGk9pGSVXzML71zvB93Xb1ionSVZq99byX/UPo92tKEC7j3F0/b9S53skoSh8X6EnJSNxQndZDrrzfOUs66pN7I4z21a23kBvfZDSXULUSnbTX2/5KNI+1T2XrCTF6Ay7oDoRmk4/4+kKoVRC3ez929a8zZKndiatcWl3Tr3ley8dHCVF0Vq/CnycrAkwWCmlgEGdbYNJ2guCKd6qKkrvXRGT2KRs5f2cueUNJu9+j6bfPsC28qKYfcwKdqfjvhkWbUbjiE6wUrJkMSo/33BfCJczSMRdGAyJC0/cj26SeKf4Egqbp+Tx6OcVHw8B0LgLg5Se78c7vgV3ofEPTmi7pjZ4FN++P9BwqgGNpiHgxzdiOLUjxwIKvGOh6mFj8bNgObVDiqkYU0b5+LFUjCmjtqgwsZ2RJa/8qtBxvaF+qk/nka+6Er0kq7Vn9Zx8+9t5LL53LGc8dBPepsetJ3qJw0mSFcOi8P91Gk9jE+iQUP3we3fwz7dPoWJtBbV7ay2NqSfTk5KROCGbSRSi6c7z1V1zcordcWb6HHZXcpFMYjdRSao5dtc5yGRinlQsmj6azcvms29VJUUD3TGZZI1Ids+ZCU4FjpKgGCX4+fWWjywn/LH72Ul2nbOZbMjs/XatE/py+r3VW7730iHbMXT/F3geqAcGA1/RWnfEN1JK3QjcCDBu3LgsD0nozSRzabNTsNvUfTMvDzo6cJeWMmjuRZz886u2XetSxVKVzPLQ8Of2SNIU6LSizfLYjneKLzQOIQHz+hRF3ccd4O8SriXlJ2h4y5vYb2d5hZqhxZy3uyNSjiHkstpOzQVjqLwleamC2kFF+EYMp1WHnnA2eNz4RgwDoPJUc6hNUSE1w4o59IupjOqA6olfonLevaEDRLkEVgJEuZGOsFlrz+icRLY7rMnmJMmKlaLwAwKar27U3DIlNhFMb6YvWDy604Wtu85Xb3HLS2ecvS2mK9MYuY5ePGkkD770gW2LR3eeg1xZ9K3e82bt7FpErVq6rBSjT+aSmuqzE18i5VRbMCJs46+zFXdYs3lloqRHuC+n31u95XsvHbIt6BYCO4H5wCeBPymlXtNaxyzRtNaPAY8BzJw5M/ljEkFIgtWC3WZuk/GlDNIlqeC72Qenb6NxRz7B5jzche2UTG/Fe/O/hhrYiHeKLzQevZ3p34pxZfSOD/3fWDe4q9/yE5Htn/xAceOLXSJj5HG46QXNYxyEK5OPo2Z7TUTMhWl1uagZWkzlqeZQ5soRwyLJThrywLfvDwBdoi6K6Ou452ezDN1U3cOH2D8n+95M2O7/sIDGutMEHzs7pdujkyQrVovCh7dHJ4LpVRjVLsxB/F4m6a0ubMnoLXPK5Th7UpZWu+5u0aI2mShL1x0uG+cgVzGMVgRFuJ0RdoSoHYGcTaFpVC8vnujrbEUcms0r1fkxet+sL6ffB73ley8dsl2H7gbgGR3ir8A+YFKW+xSElKRym8w0/nXr2DN/Absnn82exT+FGddxxtcHMvnqQxyc4+Gdd4bw7lX3sPnTU9n4+ArLx62eUU1+Xqx7Z8TNNM6VkYJheD/ZzhmXNTL56gbOuKwxIuYArv1zosUoPwjX/jkxayYQU3fu0EnjZDWhBCeKmmEGmStdipq9f0g5x5Ly46i8WMN+yLJorIiSnpO4WET/hwU0vOUl2OyOuD023L0c/7p1xmNJ4U6bjPhxHTXWozHbo616MffQ/AWmY+x2ousPPjABnrslbZfWnkq2am3lkkzPKVtuebk89z3lab7Teo7JRFm67nDZOgfRrpDdVbPN6B6LJ9k9Z8fN147Lnx2RYkSyz44V6x90XedU7rCphH+y8xP/fp4yXnOEBb6T74O++F0eJtsWuo+ABcBrSqlPAGcBe7PcpyBYIpkVDWKzRyZz30yFYSKNn70A965kR+MOip/+LQM7szAO87dz+qHfshGY983UdcJSupnGW/uirScFQ6l1B6nxDuKQO4/fHTf+ch/qb2f35LOT1suLT2QSZtSgMvC9w6FfJCTBBeCQi0hCFjNLjrekHs7PT7QslhgntUl6Thacihl3Y93gGBdUSJ75M8GddvgQSsqP4932dfhrcmtU/LherBjGtetO4Drd9WS01R3KzBo5f50JfdJKxtIdlrL4+oMtBiHVNlxaeyp9JfthNJmcUzbd8nJ57nvK03ynVrJkoixdd7i+YNEIY+aiajX5SPgYTq9FPFYsV6mEJhh/dpY8tTPlWKHrOqeysqWal52SHkZZQ8N9Of0+6Ivf5WGU1ul7OCqlfgvMA0YAfwfuATwAWutHlVJlhDJhlhKKD12ltf51smPOnDlTb926Ne0xCUImiM8eCSErj+9Cn21Rt2f+AmM3vbIyGk/9nWH+xC/rj715zP5LKG4tPk1+stg+O9TurcW36e6Iq+T/+39BRpq4AYaJuKX+9c6YrJbxLpUQe74q/nMqDXmJT91KA0HWH+g8N54C46Qrq6eaZP4cC0uSx/YZEiV0dv/OxFVSKSbvfi/1ceILdrs8MHAwtByzJKLC1zbQUM/RIYon5xLJ0hp9/pLdQ4blG4zGZnZ+nWB2bRJQ4AtlS7X1oKQPum8a0svnOXvVBsNF/+j/n713j5OjrPL/P6cvydwyPbkNM0OCMRrdkOy4aMAsIESy31EYIqIsKyCLiAuI6w7sLiBGQotcddfQ+oNFdgHZVUC+UeTSsM5XNMZEgwTQERg1GIKE6XHIZXqSmcxkuvv5/VHdM3V56tZV1bc579eLV5jq6qqnnnqq+jnPOedzWuorsgyCU8wmlqUW9nn7F5LS+mYE4LXb7H+PrO6P2US9cI2V0ge1gttnxYvKZTHtUKO/z1Zhv36/A2q1tIBbiOh5IcQqR/t6MeiCgA06phLo2tQlzcNqb2xH7zm9ro7Vv3w5zH6Nc0Ie95wDsOJ3/cZ6ebLDFJn7p79GbXkAcyIdHVh2yvNI79Z6zd48fgK3HN+IwWjUMFFPbr4e8dcexXho2qiry+UQ37sf3aNj+Ty2OciMRRDp6NAaqcUYJw4nyK4NJTVOjBkXRpSVodO//FhA9q42Mzx9NoJNa+/FWyAf3PLzulooKZVRWm5q4Dq9GhyVTCVMLL1Olu2Msmop+lwp7fCC7F5EQ4SmugiGxyZLdl1+t4MN/2Bgg45hPNL5QCeEZIpCIPRd1OfqWDtXLUfmkHF7pAkYCoctPXRmBofhWE4MEB2ya1QKeAssHClsl8SyE6Hjg5AqdbafGkbsLrlnK7n5eiR2PYrBkBKi2XNgeMqYM6hv6o1UnYGmqZen9/K4mCDLDGbHBrJLY8YLrg1P07blPWUuPEKWfaTz1EpR9b2rhZJSGaXlxm8PdBmoVQ9dpeBkslwtRlmx1JLBYKUuCZTuuvweE5U6xiq1XU5gg45hPOKnhy79qTZpuYD249N48QNXoOWOhzBbJTA1EQWGrzwPay7ZYO6ZkbD8EylX4Vqm15gPg9z5eKsiFqIj0tEBHBmVK08uiGHZ1u32jVVNYq3OIzNWbL08LifIRU/0N65EMrMPibktGIyE0ZbJ4pSxMWxpaJj6WzFaD0+FGxaLa8PTqg90hdwBWHqELI3JOy4zHCv9ehOG+pqQGSVEmgitF38Msc/dDMDlQomdUeqAqXs7MAAQaZ4lP1Vt3aLxxk5OTi1uaHF+neWmlibblYrVpHQm9H+tLhpU83VVg6Hk1TtdbtwYdEGLojBMVdLz3h6p0SArUm5H7D0LAOw1inq8ZwHWXLIBmwFE73kELekshmNhhE8+AUd/92fo/7djgVAIyNorUSnFylWqgoCtUSe9xpxAzwFlEqnUrWuByE576QqKjm9ec43Md4fJfUYjT4rKqMiMydXFzGq76Qt0A3mJ/83Xovu/L4Cp10xW1Bz24jh6CpPx1DwCxHzFUIBSe+97zXM0f8cXzAMaCKZBZw49ZXa1DQ2YGW1rN7iuxZdJyT3EmdSAoW5ieqgDqecjEEeUFYrMISgCQMesRmzdOuuSEoaLXmQwSpONDUjMn4/BBzpt8+8MRrBuYcRK/CZI9AsSspqNAAyKrJXMR487GnO2PoNZ374b80YPYH/jXBz51OVYW0ETo2rHSlSiksorBEWlKI76jd/XVSoDxasQUqnaaacqWo11Js0IumwBw1Ql3Uu7ET8xjvbGdhAI7Y3tRQmiAADWbkBsGbTlApYp2wFFzfKkZ1/Cit/1Y+WXbkXLMy8qHgUhgGzWYJ7o/1YXBwcwPTkv5hrffja6I/MBEGLvWYDBv38/9sfCyEEJAx38/NmIrVuHA81yIywHOJPVV5VUiDSYGKyhkPRYpoXDQ4BlCGR+gqyX/998743o2tSFzgc60bWpC8ldSdNDFCbjBaPkpFdyuPPODB6+NYM778zgpFe05RUKtfikFEJDTST+9e0EgGU/eQbL+1/Bsp88Y22I6EtWxBZPe+BMDFuz7ZFG+e6F7enX67HziaPQ/70ODPwsNGXMFSgYToBNSQk9azcoRmgeRXhnPlJhgoBAalQpvm52v4Y23mGZewrYFIRXl2PYuNK30gvSBQn9OCkY3yVslxfSTzyBo+/diAWjBxACsGD0AI6+d2PllNaocWrV2FFjJ5tfrfh5XV7LW7jBTfmFoNtpVTLF6tnwcg2VCIdcMkwp8CjSkSWAhFKjbMc7gff/MYx5IzlE6ic1xcGn8R6uZRXa+OTd1+DSp3IaARUBbcad05A2J8IvudlRfGfdHCSXHQQRISdyhn00ipl68iGF6dfrDeeaiAJ3n05SdUk96jBVmYjMeAT41hnTxwIs8i4twiLT77w1uML3LkNSN1/2NrRsa8Bs1XVORIDhk8Zw3Jn/n+29m4IIkfZ2vHnBqbglttW1ymXXMYvkSqkmYdBOwpXLoRJqGnYqBPp2K+VEAMiVUitUQMWTuBDjmWoO23NKrYaV+nldpRwHXoSQ/GynXf9ZnWsgb1AWcw2lwk3IJXvomBlLclfSsWfGM53nKpPl+LDyr8nky8xjQAL4xHURfO5zEdz/oQg+e0VI8dRcOFtizMGXcC3T0MYXEvjjCUfjW2cQ3mpWvHJZMsqnqD0zVuiLvCNs9P6FJiZxeu9+CAipMVeXy02FimrReqdkXpvZk8D5m6df64VrlKH2Dp6/WV6IXX0swCScELD0lMnaadefjguP6zxfACw9Qre8fw7uVt3rt5qBu88g3PL+OY68YNMXoBRub/vmo/i/sz+Pvov60HtOr7XXW/XcDIblP1dmHttIu0lJijyWBeGtwlI9YjYe2po6gI/dA2QO5+v5SQqzB9guL5i9tyw9oIxv1HKx5AJuindXE35eVyk9tV48i362087LZvVs1JrXl3PomBmJIY8lH74FwDInJ2iVvEh7u3Sle1+z9u+pSaFVrpRHzCbKBQ9VakUY21Yo2x6+VV7rwOmETp3H1r/8WOk+83U18kIUghACbdkcevbtN4pKSDxOk6kBae6f/tiaa1d5idpUXiL9d2THssy7lOSIFba7nSC7Kjyuy3uzE9IZDJPmXhcgi/ZYUWzumqv8OwCtV11p6j00lMbQ4zIs1Q2W+bmPXWud3xhgu7xg9t6yM6oZf6jlYslqnBbvrjb8ui6/i8Bb5bnZFRovVTudFDQHzJ+NYq+hEmGDjpmRWHmfZAadqwmzB2ST0Iko8OAaVf02tZHgcnLuBrMJtIzhmLz8QjETOqdGrRBCCWUshKGpMTFqDzTL22kwmLM5JUepfi5w5BCQPQIA6Nm3D/EF8zEeIuxrhrQQ+3AsDALZhxOu3YDkj69Golmlijkyhu61GxB54luuJshmHr2BL1yHgWuuNS5AdJ7reIy0NbabGFLtiLRn5WU1wmEglzMNeSzGEOx5bw/iW6/HuJjO0aujqKnB7FpIRvNlc2PbK4XxIK05mL5A/qWCwRZgu7zw5gWnStV6/3zBqVhWvmbNKGrV2GGc48XI0mMneuJlEcHPdjoxDs2ejVpbCOGQS2ZGYiqsYbK9mBC4YtCHH0Y6OjB85XnYdcIic3EWXThnsqnRl1BSmYCFjPbGdqz80q2gOu2+liFtFrRedaXhWOMRrVELqDwzVgIgOr5zqsC4bhlLf+y6nEDPvn0AhBL6ljfmAEWFML53H9qzAg+tCeFIVNsmqqvDyi/d6iicMNnUqIh8RCMQRHm1w/lINjVK+4BmRdH6FympGIapgZTNToU6pq7fUJRIhZWQibSddXXouO1WLO9/RRnHEtSGqdNQ0e5Do0rfT2ZAQqB9MoP43n3oPjQ6vZNOMCT2tsPOhWTUuAxLdUv30m70ntNrHCcSwyy9ux47n2xX+uf7TUi/oVt98LFdxXJLbCvuPl0Xlns64ZbYVvMvVaC4SzXhOMS6ys7FFI+f4ZtOBEM+etzR2PaF0/Dabd3Y9oXTHJ/HbTutRE+8hht/NLwN22b/E16ruwDbZv8TPhre5uh7lQh76JgZidvwrVLmiGxdEULiijAGRyNoawyj573vQ+9SZxM2t6GkVmGkek+CTMgBUIzg2DkevCE69J6VyYUx3H/iYWxbPv3jog9lTDY1IrG4A4PzQorHo6lRWirgjyccjW9hD87fLDB/RPHMPbiG8MuVUdBU+OY+SU2wabpHx5Tacl8dRvoDxYfhJl5IaLxNADAuJhUvcV7kY+rY85vR+u43EWvNuwR15SnMvJpqig11tPQoLYW2nbo+sPPcuPJ8P3MjukeG0T0ynSuZ3l2PnQ/egMxofLqPFsv7yBUOPN+aWnJ23lin6EKo07vr86VDAEAgszeN1MhcYPYcxFoHfPXIe2FwdFAelmuyQGYQd/Fyr2YgpYoYKcW5Kr0OWLXhl6c26Hw8p+0M0lNYa+8hVrlkZiS2xal1BKripsrRSi5chPicWdqwMot26XFTEN1tsWo/i627xWry7OZe2u5rWsxah4kipBv0aocnvZydMjRn6XO8bJQpnSiFAgCIsLz/FU/tdkPXpi4s/ZXRgN51wiL0ntPr7rnS3RvF2IlBZKcDTSicQ/vxaa1QkA/3So/b94crVO+DnU+2I3PIuEulqUe6fje4VFpltJRSVTTIc9WqcmUtUCnKqYG2owreQ1xYnGFssPQ6SJDlthUbUqhBt0KUmJ01em0scvv0uAkltQojlRl0fhZbd0v30m7T67fNh1RNkLtji4DjzkZi77Py+26Wo6TGpxA3tZdYXwIhMzCA1Be/ADx9reKJsSmWrvdqmhWkL7VIhZ3nxpXnW3dvhvrmaIw5ABDZEIb65mgNOp8EQ9SLCrLSGW6eU0tU+Y2Z7x0L2b2vNPVI1++GChV3qRZKGTES5LlmQkH0asXPPDcvBOoprLH3EBt0zIzFykjQ40lgwQqdDPlgRF6w28xQ02MXSqqelD48MClVfDT7oXZrBJeKwdFBjXer4AX6xYpBaUhF97b/RLc6x66Qy5PO1wALz9LkzSEUBWbPkdcF84B6EiwrgSAmcxjankHsI/bF0gGtUqiZ99VsASK5+Xokdj2KwRDQlgN6lp6N7jVfKf7i8tiNR7NQ0bfmCPRs6tKOL104YmZM/qwYtlNI8e55uHd6j5xZZIvT59Qp1aIe6frdUKHiLtVCKcdFkOeaCQXRq5VKEQzxW7lTQ429h9igYxiHqCfMvqFbCWrLZJGKGh9L01pmOqxWyvWT0r0mKo1WP9RujOBS0b1zDs59av+UQbRwBPjsEwKX/DiL/rEbEGmYg9ZOTHtt1DLweoPv8H7FgKuf57sBZ2i3ahI8f0TuFTQzWgBYegrdLEAkN1+P+GuPYjxfjiEVBuKvPaq00aNRZ6dM2frx1Ujd9X2I7PTSQkGkxpD/qctrizSRPByxQeeZFPm/PeRHyLzAMpw+p04JLDIgAFy9GwIstzITKOW4CPJcgU7WGc9UgnJqoJ7CGnsPscolw5QT3UpQz4Fh1OW0oVxuwhq7l3YjfmIc7Y3tBlVM/aT0wTVkUHys1MmiFedtyRm8W7ME0DSm9GNmLILUczGkd6smCQVDWlaoOTcJzGr0RzXURsmvoHY4q0Fex09tnCQbG9C1qAOdSxaj65jFSJ70D5aGSWzdOkcKj4ldj2I8pPXVjocIiT9uMm13clfSUZ/YKVPGjjyG9uOHEWnIQEBRR/zWGYRtKxRD1lDkXaXo2nrDVw0KmwiHILIR9D/cjp2PH6W950DRRbideN6sSigUi0z11izHtapwoUzLGCnluAjyXDOhIDrjjUALytfYe4hFURimnOg9RACSzS1ItC3G4OSIr2GNehEOYFqIY+FBCqxYetD0Lz/WtN6ZmkhDBss+MqT8UUh6loigpHfXY6hvDjKHo5hcGMN/x1hzLgAAIABJREFUnXgYP9UpbDoSv5DcW0TrpT8Y6SuORepnWVOBj2RjA+IL5mE8NP15hCJomtWE9ETa0zjp/PZKCDIG35IQ6Nv9hqHdrgRB7JLOVf3fuWSxvB0gpd6ghPSd6zF0/w+QOSRAswnIhiEy0/dKKpICUox1F5iJfoSE8kRN1RD8m69V7WSgmrFS67VFlWNbKaqhMwlWuWQYc1gUhWGqBYk8evfaDegOYEIhy2fatiKMXScEr1IJeJx0WeBEsh9QhS+qQyp0MfRa5USB6NAwLn4SOJIzeo1sjSeZ908d7qkidnkcmPgXDL1Yh8xYGJGGLFo7D04ZIom5LRpjDgAyIoPhCcUwSY2mEN96PQB5eQor2nJKmKVhu8owUrfbVoRGjV3Suar/XYcb9z2C2PC9iJ2p9NHOx1uRmdDuIhVJKSI/QhrKnMshvne/tsSF5N5WNVVg7HiS1a8x2fJqpBLC+hj/YUO99HDIJcOUG11h8KAmElYFooOmMOnKDAx4LnStR1bcWkakIWsMqdAVkJYpJ9ZlgPM3a714jsQvTI2ZN4yhjJ3nItbz71h24Wws/8Qgll04G1vP/CC6jlmMziWLkTIRy1EzLiaR2H6rfbt09Cw9Gx/8bRZ33pnBw7dmcOedGXzwtxn0HNB5sfLXY3btqdGUMfzSzHgqbFf1v+twY53B7Egkpcj8CEMo82TGaMwBRaujVWTh5oKxk34DgJg2diqsALiVWq8tVosuJlTkvWKYCqJQjuLN4cMQmK4dpy4IzvgPG3QMM0Owyq8LGk+TLhv0OR7U0gKKRjX7UF0dWr/8daPBrIuhz4zJgxbm68RjHIlfWHqCJBNklWGfPOt2xMdfRSpMShiiJBRRxuCRaSPMaZ7byQdPwGW9ISwcUX4QFo4Alz0tcPLLujDW/PVYXXtByGTqXDqDGYDWqFL1f/foYcTHCO3RmLPxqTOeDGIohe1NBFl+hNP+KVDId+y7qA+9B8Py4vNFeP9kix0DV1+D/r9YXl6DoQhjR4NN/qhfeJLVdylbHuTCFFNZsOFePFblKJjg8BRySUT3ATgTwJAQYqXJPmsA3AEgCmCvEOJUL+dkGKZ4yqVSGXTdJL0CqavwTlXdr8gWeRHdfc3T/+/YqylT0NJjEoLpVFVRTyFMUp/nZlCMVDG08Q6Ejmh/fENZ0oYqqowwWfihGk34pSSk2BC2p+r/7vx/jtCFy7Z2HjQWGq+rQ+sNNwK6e++mf6T4qI4mW+wo4Cp80G+81GgqYSijJ1l9iWx5srEBifnzMfhApyE31W3tznJi9Q7Uh8PdcexOHP/Hb1Z0aG0p8RTGW2aCSm1wA5ejKA9ePXTfBvBhsw+JqAXAXQA+IoRYAeBvPZ6PYZgqxGxyFVQ9LbXC4x/+65/xtxPfdOSJkYVv5sIh1GdCePjWDO6+S+BrE+ucTfr1ClpmSCbIViGdBEIsJxDVCcHU5XLomVDCC63y3PSYGttjebXIJzuQbrlk2uhSeXrN0LRfF1Kcfr3edOXb1aq4zvsXW3IY7avHEFkQs1Xjc9M/UnxUR7Nb1PDLk+0au3BZKyTevfROYOenb/Dd4yF7Zh2r9a7dgPQbzdj5eCv6H25H3xNH4Y/Pt+BLd2fx0K2T+NJX38CP7lk/9c4oZUFvL1h5En/44pt4auP9uPl76/HkD/8VG7/3r1jygxsrPrS2lAQZURIkQXuQnb6fzcpOcDmKYPHkoRNCbCGiJRa7nA/gB0KIP+X3H/JyPoZhqpNy1dNy64nR12+jWAzh0VE0jSl11Oals6BvPop063HOVj1V3idzxUfjBNmsIHd7Y17Apu8RbP6PLyD6/Gy0jADDzcDk+yaw5rO3ATA3CGXbrUVllFpvqf98Cjhm9dQ1Fzy9ZuqPZmGZVivfANytiku8f7GPbUDMgVHlpn9MUd9bOyzERZyI+vhlMLhavZd4IdNvNGPox03I3HOs9fd1ixTTYkMAkJ9orl8PwLvHw03NRUMzX69H6rm5EEeU5zs6Gsbf/Hp6+WXhCHDxkxN4JHILum/qrppC71YGyZZ3/B989vlHUJdVrnnO4cPY91wTZiErr9UJ5T3quGh8DVAthrueID3IbryWgdaOc8BMFWQJOofuXQDmEtFmInqeiP5ethMRXUpEO4hox1tvvRVwkxiGKTW+1zJymJ9TjCdG7d0LNzRATE5qPnezUqvJ0zqqBcnmFu0OJmF6dgI26dfr0batCfPyeW/zRoC2rfVI3x0H4i1oy+YMxwTkhpYTURmza3YrtGM24Xjppuvwm69c435VvEhBITOD001hcMfeRBtxESf974fB4Hr1XueFTA8djdRzc5HZm7b/vm6RQiY2JI5MYuj2mz1fF+C85qKeoY13TBlzBfS+9LoMcHrvfgAevYFw54F2m+OpxtQgGRjAZ37+wJQxV6CgBqtt7J6pdsR/EUdqNAUBgdRoCj+6Zz36TvnrYPLLSpR7aUWpI0r8IkhD1I3XMtDacTbMZEGWoMsWRAC8D8BaAPUAfklE24UQf1DvJIS4B8A9gFKHLuA2McyMoZJWVvV5bkXjIj/HqyfGyw+kwTs4mUZ8wXxg9hx0v2Wdq1K4R2b3TjYRFZM5DG3PIPYRgZ59+xBfMF9TMNzM0NJ7OMxq+mmuOe9x6k7vARYuQmJuy1TdxFMWnYLECwlc9/PrDO0267eWtFzQxOo7XpDlAX6wP4zP/OIg+q+38T7BwWq12iNHIeRdU9OoPCCa/h8YUARwVPfAL092Uav3Ki/k0GlrIY5ovVOm39d590wVSPemi7iSIjDxkDodWwvyokhuvYFqjyjFYsDo6NQCkZWHw2uOp5XX1yz423CP8ka5flHspJezuPipDKL5GiH66/BcE9DP3Msiy26UK6LEK0F6kN3+FparHIWVIEute+mCNuj2QBFCGQUwSkRbALwHwB+sv8YwjFc8Cz9UKi7qu5mFLjr1xJj+QDYKZQXZYpIg9Q6KSSSO6kD3516yPbeVgI153psyKSsoMCbmz8dgOGRrzKuN7Z2nyYVhpiYFuklX91tvoHt4L7DuG0g2NVqOObP+LIjOLBwxfBTIqnj30m40/vR5RO95BC3pLMbqCQ2TAqGMohJqF+5pZhwNXH0Nhm6/Ga3vfhOxxfmLEdnpYvWaGoPTYYnq/g9K1MDr6r2r7+vCYSMNWamCbKQh4+jcnrAwEpzWsESI0L982tBf9pNnbL+iN/rFsLGYvZlBnHghgff1jeL8zQLzR5Tn48E1o0jUOah/CblBYodGJVYVOaBf/Dp/s0Cd7rapPTWexERcvNtt8WAcegnjLSdBGqLVEm48kwVZgg65fAzAB4goQkQNAN4PoD/gczIMAx+EHwKmaFloF+p7XmvvSUOswgKtKw/ATkDAlzwtE0xDglSTsu7RMfT+aY8is39Or2Mj3jaszGLSZTfmZMcejwAPriE8uIYwrpvz+7kqrg5hu+5LJ2HhNzZhXjqLEICmwwKhjHZV1yrc08oIyuxNI7W9AendigBAIX9MMWiU0hgDz8bwh0fbpWPfLnxQ/9ykvvxlR8+R1zAy2+/rQ+WAqXDY1tVRUFgbBkzhHFpXRxE4FuNVNh71/mkBgHLCtciElXqpGtlYesev3sRlTwltKZGnBN7xK2dhY/oQdztENJK/F0aBH/3il76Ei/o6PIuJeFFW1eOx7EaxYbxBY/X8D228A7GzP+pfaoMKr+HGpWImC7J4MuiI6CEAvwTwbiLaQ0SXENHlRHQ5AAgh+gH8L4A+AL8C8F9CCPulaYZhPBOkQeEVT2pcLtT3vNbeM+T+NQHtxw9PiwcAppMEP/K0zJCrcQrctyaCziWL0bWoA8nGhqLqotnmO1pMuuzGnP7Y+2NhfOsMwrYVYWxbofz/W83KJDrS0YHBz5/tWKHUCn0e0Om9+xGamLT9npnhZmcEqXOSZPljECFkJ+B67Muem+GHHnb0HHmdkFl+3yZPMHZ5XFEgbcgAEIg0ZNC+egyxy+OOzu0Ji/EaW7cOg58/G/tjYeQA7I+FMdL919OGUDhsCFF0aqA49Xyqx1Jhsv75x3MGL1hdBvjkz5zVowS0Bkmko8P8/B0dOPqWWxC76xVpLqp+UUxdwkVDKGTq7XQcNu1FWRXA5ntvxLb3r8TLf7Ec2x7KYnMqZtypGOOwQnDy/Kcf/SFar7rSd0PU9zz4gLj6Q+9GfVQbPlxKQZZyQsIkX6JcrFq1SuzYsaPczWCYqsdMgXBKKbGMmIb1dXTYhzPpQ2kAJUSoSNl4V8RbYFzDBwBSJkMq9CGvQCFPqx7Rt9Kew3jUoXmTc+vxXycexk//cvqHrC4nEH/72ehe8xXDdz3lVpqqdS5G1+IOV2NO1kd14TrET4wDgOlnbkOG9c/Cw7dmHK1mqsdj+s71GLr/B8gcEqDZBGTDEBnz3D9AYPknUuh/uB2WZSsk5zLD7LkxHKsJWHZmCogtQnrWWRj6/vapPK4QgGy6uPFnGg5qMSZwVX4N10U+k69hpxZtS551u+UYe2X5cpDkcRcEHNsvCTZSXePOJ9uROWTdNKqrm5oU60M0ZZie1wbZsdXntkP9vujeOQeffOKgowWRAo7e64Cnd/vme29Eyx0PYbaqWRMRYPikUaxpV+VqqsdkleH4+Xfa3zVKLalcEtHzQohVTvYNOoeOYZgyIRN+cBNuGCRmK7ZHBgbQtanL2sBwUqw6KCSFiKe269ALm3TvnINPPn0QoQlneVq2TVHlXcmM9/EQIbH3WUOh7iCLavfocugA6zFnJf7StanLNHzTrUGn9xzua5bn66lRe6/Sd65H6q7vQ2QJAEFMAKAMwnMakD0oz80ohL9ONuYQHZWLgqhx4sVwnO92SAAQSP9mL1LPFdqt5HHl6urQ8dXbp4yInaetnTacPr4asSOPmT5XpsJGTkLlHJZ5SD/xBFLr10+J/ngucWAxXq1ChLuXduNAcxjzJII9B5ol91NnjLSuPIDUcy1TfQ8AiEQQbmqaNqg/vhqxV68D4hdi6Ml2CJsIzQPNYXRKCp7b4TUnTJ/Pm37/tMGNUAjImi9suArL8/Buj97ziMaYA4DZGSD6YiNQMOhMVIWrBa/5rjOFcgmylBs26BimRrFTSiwnVuIYjgwMNzXA/MRicihDPRHaedpaZCb2az73q0aQm/Bau0msLRaTrsK33Yw5M/EXP0OG9eI4D64hXPaUTtyBcghHBbKTEcOEd+j+H2gn5gAgCCQOo+NrXzV6P2ZF0bpaMf4e+mAE5/6vUUhCj5NcNqciHgVjUgn31LbbUsDiru8Dxw8jtkQVNgnYP2uShY5kY4MiyuPSABm6/Wajgmu+xEFRz4nFeB188SbpVwpj7DunClz6FDT3bjyibD+psGHKK6e9/kJY9tBLc5EZJaMRpTMAFSPc3JM7EQX+59QcBKgogSvfVIZ1x+pffqzpfpGODvfe1SLf7WZKuS0jgJIbWMKFv4Bw/PxXmFAJUxrYoGOYGsZKKbGcyNS4CuIYQPGemMBxuYKsDh1zVA6gSNyoefphKKVfr8fQE0chk8oh0n4UWt9Zj1in8plfY86rQikwHSqmP862FWHMymXwmZ9mER0NTStPvmfBVDiW2nul3DvjZDtzSNh6P5IPdOJAKDOlWHioDqifBKKq+SfNijryYsjVC7Vto3AOrZ0HlfaZlQswE7DIEgaebcHA9pbpPnGiMKhb6Eg2NqjKZghHBsjUs2JSysBLiQOz8Wo3xv54wtH4Fvbo1CYJu07Ir/7LQgRVxJYcRmzJuCEcG4BBtMNMCRRQcvv+59Qctq2Yvp+V8o40VT8scdjfcEzuTR2OheX9X4U4US/1U6gkKMVdJhjYoGMYJhCs8rTUk+AjAwNTEyX1hKVc4i22+WVuQsccSIf7sZrqJrzWq6FkW4PNJ7yGDMvy8wq0R2P4UMcb6FynmuipPK3Geyf3nESalO1670dyVxKJTV0YHB0EUUH0Zfp7J72cxYWbBeaNCMVwOu4QYm+zl9U2GI+NAk1HjeJQqk5XEkE5lmm5gPZ284UEoVxTZiyC1HMxAHshkZbQolvoSMzX1kAEjAaIVY02GW5KHDit/2Y3xnre24P4eBzbVuhy7Apj8Jkbkd4JDPW1SvsfgLmghy5MtbXzIFLPxTQCOoU8t7/bvx5CMgbV78hyTb4rpWbb5KXnYkKfQxdVtpeaoO6FbPGo6dRTcOhnW3w/VzHv+aCumw1LZ7AoCsMwvmMldqFfUa4k8RY37bbDSQK7G2ECO5wKnSR3JfGje9bjnJ9MTHkdNp02Gx+69GZH1+hJ0MYlXsRbbMeVhUiHo3sXFmi/4uOIfe5mQ5vNDMkCdbkc4nv3T9ULBFCcWIONh0gpmaDN46JZUbTffPN0IXMbIk3Ash3uhDg6H+iEkIgHEQh9F/U5XuyY+l44h/ZTw4oSow3Ojy0QaSK8+bcn4JZ3pEzHmNUYTH+qzWiEhXNoPz6tGHV6QQ+bgvPp3fXSEM2uB09GatLooWzP5ND7xptID3UgtTWiCVX1891ih+WEu8ji3sWw+d4bp2pLDsfCmLz0XKy5pLQ5c14FaCoFt+/5oK67VvqzWNyIorBBxzCM77gx0vw0orzip3HZv/xY0zBLkCSnpkSkn3gCe760XqNSl5sdxaKbnOUomV4XEZb320+4LfFx8mdnVFhhee/yhkDrxR8zGHOA+RgKUQhCCLRNTqLnwLDWmMu3rKjQMH2fLesCdvYqf9fPRfr3RzD064ZpD9Jx44j1/DvSr9c7MnwEgE9cF3VlUNs9R07V+pQSB9NtdjIWnB9bwcwwd8LOVculSpaRhgyWXThbO34lxreh4LzJdSbvXIl4g8B4aNpwVC8K7Hy8Ve6JLYfaoXo81s8FjhwCskemPy+VIrGEID09U8c2GXtW98LQLhtxolLg9j0f1EJfKRcQKxFWuWQYpqy4ydOqJPEWP4U4KiW3RM/QxjsMkuOhiUnH4iym1+U1dFQ/4U2/gXTiXzD0+68js2/E9QTMS2ipl3tnNlaEEIohuXElYDDmUFTNQADWIcAbV2Lr8nEkToxgMBJGW4bQcyCD7mduRCzvDbRTK9zbDAizPDgTA9wulNFxjbaGbN4wutnxhNZtTqrIEobu/8G0QediUSEzatKGwxGjt1WXM1coOF/w7mXGItjzXAy3PZJA8sWbNO/B7rf2AI31SMxtyd/HrGZRwCpXsqTon+HD+437FOp2lthAkYUQDlx9DQauvqY48RaLY8swuxfS0MZixYl8xO173uz6vI7BoI5bi3gqLM4wDCPDbVHt7qXd6D2nF30X9aH3nN6yJfr7WQzcayFnPykIfPQvP9Zz8d/Arks24d3eoIhhuC0+D2NBZMB5Dp7ra+x7RDHU4i1oy+aku0yNobUbFC+FmoDk1JOZ/YgvmIdUNAJBhFQ0gviCeUhmlIm2uvh0x223Gq5ZLVQETOfBAbAsJt69tBvxE+Nob2wHgdDe2K7xuDsx/qmuDq1f/rqh0LUdxSwsKAqT1tckP5e8YLd0uy5nTlZwPnQki9N792sM6OSuJBBbhO7RMfTuGUDf7jfQu2dA4+EtqJoa21FitUPdMwwoz/HOx1vR/3A7dj7eivTu+rIU95aJABVw+25xc+wpQiH0Lz8WO09bi/Sd66feF0NfvkYqTjTUN2d6Q8EILiFu34FmY83rGAzkuKr3dfqKY7Hz5NXT96bIMVAJsEHHMIzveJlMlxM/2x1btw7tX7kRkY4OJcSyo6Mscf+FFeDMwIBFGKH2BzK5K4muTV3ofKATXZu6lEllHs/XpfoxxcaV05NlBxNeMT6OgS9c5+jH12BURGOIpw+j+78v0J5X0q7Yq9eh/R/OcHaNOiOgZ98+1OW0/awZQ53nKiFnscVQ5NQXa0LQrPreLYn58zRhegAwHgohMX+eYV/9fX2rGfjWGVqhIgAYPDSg3LtHLzfm7qkmnlaLNLLJIiIRhFtaPD8rlsc2oSBuIzNIrCbTria9Og+smVdtvqo+4pQBLVsEULej8yAorF1IKMvike4ZLnghlXBQmhLaSQ/JDeEgsVuwUpfz8PvYABTvd2Fh6q7vI/2bvQDE9GKC/pj68VFiI9jtez6ohT7fj6t6X6d31yH1s2zRi4aVBodcMgzjO5UURukGv9vtZ+2nYnGyeqz+gXRSeLzo65KEVU6FE+lqmZlNeAthgTLVNZmAxZQAitl5O8+Vfr61+TtIfGYxBicjaGsMo2dFyFCkHYDBCOgeHUPjq1FEX2xEywjy4gzrsMaBUqq077deDzx9rRJ25yCfRp2P86U5wqAeCwCDYblqp/q+9pjkwbVlsgCEQdBjugH2E0/Xha5dhEHKjv3mBafilthWvONXaVz+lMAslWAmhQVaL/64ddtNtru6Dl1pBzMF0n3N2r8HRweN5VJ0uWmxJYeBcBRDvz/aWXhyUEIlumdYuiiTDWGor9leOdVnnNRwKzaMz/LY4bAhlLnggYstOWyuRKv3uhYbku0BN+95/bNAsRhCAAauuRZDG+8oOqTV9bvCDtX72mzR0I/asOWARVEYhmFqGLfiLIGqjm5caSjADEDxUukmvGZCD3oKeW2W4jqPXWt+3qteMrRLqaOm9W6ZCvXEWwCV+Io+Nwpwrspm2veTGfTuyU8YLUQlZII34xGjp83JvZSqoZ4CfOgomaCLimLUOq2QKXm6ENbQj4uTXs7igs0C80cEonpxG6vxWbgmL8aQ6rsyZUpX96rYdnjsTzfH7n+4HdKSH34IKLlE9mzoKTa/2UqJceCaa03evwLLP5GSvy/CAu3HD0+XvyijkEwxVLQypep9XUnj0ww3oigccskwDFPDmOYgdHRgef8rWPaTZzQ/sn4Kwxiw8oDoQhFbV0dBs6K2hyysqideSBhKBUyFrNl5XnSfJ+a2GEMV1fljanQr51arvnaY9n1E5WGzCAF8/Wu3GCasdRng/M3TE0p1+KdVeOfJL+dw2dM5LBxRJgoLR4DLnhY4+WWLRWAXuYCOQ0tdhkHq0Y+LbSvCuOJzEVx50zFYtqNfq25pl9/oMsfOQOe5imEYH0bsrlfQfvPNUyFtk60tuP/M2RpjzjLcW3UsV3mGsjy3ncDOT9/gPY9I9wxPhbLqKHluH4CtK0L41ukhvNUM5ACD/q2XMD6r8ETT92/eAxdbchjtx6cRacL0d6/4OGLvWQBZSHY1IIsK8RLS6iuq93XF5J76BIdcMgzD1DBuC/96LTxuiS4kS7Md0IQixgBAFT5opsJY+PG1NETtzqv7XGNA6Y+lR+dZ9KI4aNr3Gd11mxiokSF52YP5I0q5BnUIsV1orVQNVRUqNgWFAZFz5SVyEtZrd61Oc4pcLVDoQxv112RlXBYx4daHtH1oVxK/CzpM3STPTYmgFY4KSFuieoZb3yf31KjfPaUqGp14IYHU8ix+ulyZ9p70chbnbxZYMAJEOzrQdOopGNp4BwauubaodsTedhixdX8G0gNALAS8TRkn0vdvWKC18+D0d5cBsX/+sm4MuS+jUSmUWpnS1RhSva9bOw9KoynKIVzmB2zQMQzD1DBucxDsJOc9oTN+AFh6ddQTXrMwnsKPr6UhetxnrM+7dgOSP74aieYGDEbCIBhX8KeOpUdnBESaSF6bzMGqr7Tvczn0HNAZaib5NHubFU+ann3NMNTes/Jodi/tNp+UqQ3WIkPBTM+9+VpFtEZtSNkZ4za4XqCwKgPh0bi0o3tpd/B5xk7y3HzKI7J790gl+70YkxboDfhtK8LYtkJZ6Pj5vCu9tcMiRze2ThlL6j5oWtmBoV/swMD2lumaliXywJXCgA6stI0E12Oo81ykf/4bDN3/A2QOCdBsQrihHtlD42WrDesXHHLJMAxT46jl6fUhlnrsJOc9YaPwaHcNVqprlgqldsqSTY2IL5g/Je+fI2OomNPwt9Ybvlq0KptUmfPAIW3OmoUB/HTXPIzrlmnHI8p2PXaeK9NQsSaC11Aw03OHAEMoo8cyD74q7poZkWUQrCgaXX8GXcPO6t1TytA8q5I0ntthExas7oPWq65EestL+UUfZfEn9Z9PlURZUa94HJSqYylL9pjdOzM15PQTTyD1n09N9b+YAHKTAh1fvd32t7HSYQ8dwzAMoyFQT4GVB8QGK9U1W4VSi/MmXkhgXBjFEkIUghDCVfhbMapsMnVOu+LdMk7+9Bdxf0YnZHLabHzo01807GvnuTIN1b3hRsDjpMdRaGlhQnzVS9j8dBLRx36lqIY2A5MfPwFrHI4hz8q16v6vnwuEZ02pSwIIrIagHt88Kz56lL1SytA8q8iDTOpqb+1w4bm1Mh6DNiZKdW4n70C/xrPpPTJRQy5n/wcNq1wyDMMwM5rOBzohJEGWBDKEKvqNpTpnEUa1U+MwuXAR4nNmaQxZ/Xm9TLqsviu95lwO8b37dQqahM2L/xktdzyE2Sp7eyIKDF95HtZcErAhJVOEDEWB2XOAwwf8lfy3IEjVwHIqEu48ba08NM9EbdJybDvA7Ptu22HATB0VmFbwzY8RU9XhoJQVVc+8ouoooQSqjur3AcViwOgoxOT0Q13smDO7d3oK97Lk/e8RNyqXbNAxDMMwM5pASzV4PLfdJNbxJFdinCSbW5BoW4zByRFfRThkRkJudhTfWTcHyWUH0dbYhlMWnYIte7Yo7c7m0LNvn7EcQmwxtj0MzEsbxXCyBIRhLLvhK07KGJQAzwaHDZ69JUWWUHBjTPq98FFsO6TIDH81qlzToO+lvl3pxL9g6MU6JbSWAAhjOHkg51Yh618ZxbTD6bELBltJ+98HuGwBwzAMwzjE1zwrl1jlshUmsanRFATElBpkQeLf7nMNkjyf7pFh9P55GH0X9aH3nF7fwmxlYU2hiUmc3rt/qp2PvfoYet7bo5x71QZ0H9EtLudDGVskxhwAhAUCzQMC4FoEJf3EE9h52lrv8v86gg5MgVe9AAAgAElEQVRN1Oe5AXB+HTalHKzKU8TWrcPg58/G/lgYOQD7Y2EMfv5sqRFlWZbEh+u3ys+1RZOjK0GVT1fK/LL03XGktjfk63lS3pjTPmdW57YrLeJ0vMveBzKKGc/6e4ewPCe0EEJcyv4vNZ5y6IjoPgBnAhgSQqy02O94ANsB/J0QYpOXczIMwzCMn3jOs/KAVS6bnRKl3eca0nuQbGxAYm4LBiNhtGWy6DkwjG6VceI1pK2A2cRsvkqBU9NOi3IBw7EbpR46NYHlwLhQ2PRdsVHl9Yo0tpcsz831dVgIgiSbGi3LUyR3JRGf/QTGryAUpqN14ScQ3/U+w7gLtD4mrPNzHVHI0VUVrtaQf87c5th68Z4ObZ+EyOqn+QSQABCSqo4WzjW5MIYfnXgYqeXKs6e/d27GiWNDLRRC//JjXV+nGzXkYnKcqwWvHrpvA/iw1Q5EFAZwO4AfeTwXwzAMwwRC99Ju9J7T67u3yg4r76DdJNbNJDe5cBHiC+ZNKXmmohHEF8xDcqFinLjy9tlgZmjsazZvZ7KpEV2LO9D59mPQtbgDyaZGAMDkpediwr6+fDA1rlwobPqq2KjzerWuPAAKO/eseMH1deQXCroWdaBzyWJ0LepAsrEBSO+x9arZfa72AP3HXTmc9LLRsG/L5hQjauNK5wXeg8SBEqpT1WGvqpSKZ06CIMO59eeKDg3j4icnNH2uvjdm4+Slm64zePQcLzxks5697k68rW5Un6sJTwadEGILgP02u30ewPcBDHk5F8MwDFMcdqEzTPmwKhNhJbWu/tfsczWJuS0YD2l/8sdDISTmtiif+xjSJgtrGo8AD67R5u8U2mllTK65ZAOGrzxvKiwva0wBAuB80ujqWXBRZqOYsEjTtui8XrElh9F+/DAiTSguJNAFbq/DaqHAy4KE3sCYl87i8qeFxsCoywn07NsHWainHW7fiY7391hmQ43XRYLIgpjj7bJz1WWA8zdrFxIK98xsPLSks4ZnWPY+QCSCcEuLaZikl/IVtWqw2RFo2QIiOhrA2QBOA3C8xX6XArgUAI455pggm8QwDDOj0IsJ6ENnGO94DVU0KxNhV+TdTRH4wUlJxXEAg0eGgXgLBpcsUiZX+s+LCGnThzVNLozh/hMPY9ty1WRc1U670NE1l2wA8oqWdiFVVhT1LDgss+G2mLK0LVuvB56+Ft2SMM/YksOILRkH4sOGz/zE7XUk5rZgfDKt2aYsFMTQNqvBsjSGVbixzMCYPQlc+LMQfrGC5EI6hVw1m/vldhwkdyXxo3vW40tT5UDewKbT1gOXSva3CB92i9fcydZr1yO1fj3EEZWa5KwoWq9d7/iY83WvjcK9Mxsnai/81DOcF5YyC3PsX36s9Nym11mkCE+tE7Qoyh0ArhVCWAbACyHuEUKsEkKsWrhwYcBNYhiGmTkEKSbA+BuqqMeuyLubIvCm3rxMFoDQ1oBz8D3AWhRBvUreueWX+NClN5u208xoTI2mpEIaxQpYBPksuBVbkLZFTCIx22K6VIIC5q0fX20M7wwLtH589dTfam9VSmfMFRicHJGGE3+wP4x/+8ZB9C8/Fv/2jYP4YL/WOzNdG04+mZ83klPCov+0x6iKCpgL2ahwOw623ncLLn5yAgtHlEnzwhHg4icnsPW+W+Qn6DxXUUGNDyv/FmlsmBnRTr3RsXXr0H7zzdpn5eabpc+KkzBp9SKMUy984dm28pq5uk4bEZ6ZTNCFxVcBeJiUVb8FAM4goowQ4ocBn5dhGIZB8GICMx1XwiRFYFfk3WkReKk3L5dDzwHF49NzYBjxBfM0YZlWSp9uxTOs2mnmqQHk3pNiBSyCfBbcii2YtiUiV+krVQHz2JHH8OLJY4i+2DhdyP24MfzFkccA3CwtHyCjrbHNIDbUvXMOPvn0QYQmlDEXHRrGZU9H0RhpnipnMVUbrv3r1p5CF4I1ekz7/tCAko+n8/qc3rsfdRntvnUZZTtusj1d0bRedWXR3ugCTp8V2blys6N4umsOCAcNkQf68b6/OYT/OTWHbSu049dqQcjq3KbX+cyNSO8EhvpakRkLI9KQRWvnQcQceGZrnUANOiHE2wv/T0TfBvAkG3MMwzClwyqsifFOtRjMBiXPyUlF5TLv5Sj8m5jbgsFo1DZ01Cq/x62xJTM21RgM5CJDroJ+FtwYmqZtkXlKdcWpAf8USfUkM/sRXz0X4yeqDPvcHKXoO+QLGHrUCwFqQ37naWuRmdDKLoQmJvHpX9Th1pu2abbbTvLXbkDyx1cj0dwwrdo6fBDdR0alRpka677Pe31+eAXw9LXA4QNYMCL3IC2QRzH7hnSR4OOrEXv1OiB+oa/hhmYLEreuW4dbLb5T+F5yVxLP/yIOOAj/dnpu2bOU/s1epJ6LQWSV8ZkZiyD1XAzAXkxlBs7QkEyvZQseArAGwAIi2gPgBgBRABBC3O25dQzDMIwn3ORZMe6pJoNZ4yXbuBLQhax1j46hOzIfuKrP9lh+1kZTG5tmnropA1lfxLkQcgXYTtoq6Vmw85hOISliHmRebGL+PIyHtGFz46EQEvNi6LbItQQAAlkal27GjN0kP9nUiPiC+RgXSn5YKhpBfH4M2Lsf3epQPMAwLpz0fbI+isTcOgxGFuHO5qzUeMu0tkivx09ibzuM2Lo/A+kBoP4wsL8PyB5RPnQx9h2dy0PpBq+lX5yee+iludAncYlsCEMvzVUMOsn7IZ34Fwz9/uvI7BupqTIFejwZdEKI81zs+ykv52IYhmHcU84aazOBSjISXLF2g3biA7gK63MrnmFHwdjs2tRlbSA/cyOSswiJozq09fQchFxV0rNgaEu0GT2Db2jzwkzuR5BhvoNhubE2GA6jkGuZihqnju2N7ejNi1+Y4WTMOK27lnghMWXMFSiotk71oYlIip23OtnYoAk//u4awuVPCcxWhV3mZkfxtqu/aHm9jjHzKOmNk8MSUXmHQjCloPvQKLrfGMhfRw5416jv58iYHHJqu04hNr27HqnnGiCySq6n5/qQFUzQOXQMwzBMmXGaZ8W4p5KMBFd4VOPzI79Hhp2BnMzs10y2CzL5yIcEArAMuSrls2AXFmloi8NQsSDDfNsa2y1DQd3mWqqxGzPSvMzrrgG+/w+IvWeBpj+c5CCmd9djqG8CmXuMxaqtvNX6Eh9KXlgWF24WmHeQ/PXyWHmcZUXbZTgQggkcD55zGWaGfaS9w2RRoCP/RW1fDPXNmQrPLFBsaHilQ0JIKtqXkVWrVokdO3aUuxkMwzAMU3EElTtVDE69KW6xusau+1YiJfEitWcy6H0jBdTPBY4cmg5LAxRPl0n9uKDaKhMPqQvXmaqQusHMi6nxkhWZRyRtdy6n5NCpPFhOcy31WI2ZnaetlU/WGzJY9pEhzX007YPJDHr3DOQ9MzHNZJ5mRdF+cgax1gFlnADA4QOGMdO5ZDGEJKyUhEDfbp/zsjauNBF3WZw3ThzM0SVhuSXH6jpcts2sNEn7V24EANPPYuvWGdrR/3A7AInXmZTi6pUOET0vhFjlZF/20DEMwzBMFVBpNQW95NxYYeVFswsJLGVYmtX9CDIs0jbM14O3xOBxzubQs2+/JhTUTa6lHk1OWCwEvG3aA2WaYzeW97qp7mPPe3vwo3vW45yp2nDAplMIHzpKyYOTemaOTGJoewaxj+jGyeH9QCgK1M8DDh9AW1YgFTGOM41oil+5a2betYIhLjOS1JRI/dQWq+twiZXg0rKfPDO1j3QhSRdKHmnIIjNmNHWKDQ2vZNigYxiGYZgqIOgSCdWAXUigKQGEpVndjyDDIm3DfGWhei6MWo1BXTAO1RRrRNgYmqY5dg3KvVWHUL4jFsNlhzII5fPaFo4Al/WGsOjkuUDrYekkHlAZh3pyk8CsRuDa19CzK4n41us1OXoGwRoX/WnpVZcYbend9Rh6aS4yo1lEGo5C61+OILYk32ehKDB7juJZ9Oop9FMN0kMZCX1bMgNyY6tg8FsuJOlCyVtXR5HaGtEWV/chNLwSYYOOYRiGYaqAaimRECSO1SH1BFCU26ogeohCkKW0+KV+apkLKDFek40NSMzJYvCBTndhkh5zLTXYCNpIc+zCObR2HtSFUAqI4WGEdIcPHcli6HcdiN31CiJbzMI3LQz/fL/Ziabo97fC1quu8ygp19kypeSYGQ0jtUMJD9XnEXqi7xFF/fHFOmTG2hBpmEDrS/+CWA+KO74XkSWdoR9pyLjzqskM03yYZwwALMJ8KymE3Sts0DEMwzBMFVDuEgmVMPlxEhJoIKCwNKuC6DmRM2wrmfqpzluiVW0U7kN1O8/1xYiwE7TRlioYUIpG571TOx9vNYRQyih4cayMQ1NURr9diY/NqZhSfP3h5RiOhTF56blYc0kRiqQ6g1kqy58Bhv60ArFvP2N3+Y5J3x1HanuDtp7b9gZgdhyxu4q4114Mf51HubXzoDH/0cyr5iC82MyjV2kh7F6xfzoYhmEYhik7Pe/tQV24TrOtVEZCYfKTGk1BqIyC5K5k4OfW0720G73n9KLvoj70rtqA7iM6T1ghHwqkiDIEJIgiux96QhQCgdDe2O6LIIopfY8ohke8BTgyCoRnTX2kV20Epo0Kv0nuSqJrUxc6H+hE16YuzfhQatzp2hEKITF/3tTfsXXrsOwnz2B5fz+W3X+T4pUCmYZQ6il4cWLr1qH9Kzci0tEBECGyIIb21WPToYt6rIz+tRuUz/NsTsXQsq0R80aUSfS8dBYtdzyEzffeaPiqI69657mKRyk+jMyoPEe0mPqOVgxtnzTmGGZDGNo+afINB6iuA1e9pH3m1ONz40rl7wI6T2dsyWG0H59GpCGj3LuOjmnREz1W4cU2WBnb1Qh76BiGYRimCihniQSzyc8Xt34R1/38uvKFK/kZEugAvZfyrHeehS17tph66oQQ6LvIvXiIK2T1ylQiH2opfzWDJm12dD5Jf9t5PMwFbeTb1Z5BsxBKNXovjsEzo263WuXSbszoxlj0xUZNTToAmD0JRO95BNB56dx61f2u72iGeY6hT2aBvq/VyrN6L5ok/y625LBizNspZHoQY6m1EHY26BiGYRimSihXTUGzSU4htLCs4Uo+hQTaITNYHnv1McRPjCPxQkI+cc/mFK9EkIamzEuhEvlou28lUhKbri1bRNkqixC3xB/+yzK80FTQplFurKiN5+4T5+CTT0QRmlB5kCIRhJuakE2npWUzpCHCxcr7q8ZYy8PLpbu0pI35ebaKpDqCqu+oJ7IghszetHS7Z9wWRPeSf+dBjKXcIex+wyGXDMMwDMNY4mSSU83hSk6wCtGShsPmBHr27YNG6l4dauYXNl6Knn37UZfT5vTV5ZTcQ9dYhLjZeTzchAzrQ3yfXDaCb50ewmRry1QYXsett+Bd23+J5f2vYNlPnjEYc0GFCA/H5B5P2fbupd2InxhHe2O7o9BbQ6ioVbihB1qvXQ+aFdVso1lRtF673vQ7VuG0GtwWRO88VwmLji2G6zBpXTgsACSbW9B1VMt0OzdfLw33LGcIexCwh45hGIapSipBpGOmIPM0yKjWcCUnWBkscrGWfVqxloDq4dl5Kboj84C9+5Ri4Gp1ych89+eyMB7bFp9g6fFwEzIsM55/ujyL373rIHr/ZKxh5+T7fpX4mLz0XEzc8RBmq5yFE1Fluwy3XvWtK0JIXBHG4GgEbY1h9KwIodgWm70jtQI0knpukuM4FhBxWiJE7UUr1suuC4dNLlyE+JxZGJ9MT7XzSwcfxW2xHNJzFylj/8dXoxtAd/67tfIbQjJZ3XKyatUqsWPHjnI3g2EYhqlg9BMMQFldDVR4YoajnhwSkVTJsb2xHb3n9JahdcHTtalLarBIrzneAkA2vyJFNMJP9CFugOK1KHg5bD53tTCycaWJ8bgYybNu9+2Z7HygE0LSfyQE+na/YbxGp98H+ZLTuPneGxG95xG0pLOWKpdu8fO95uexzMZ+oTyHZtyYjRE1FvfOgIt6eWbtVFOXyyE+Ruj+XJHhtyWEiJ4XQqxysi+HXDIMwzBVR60plFUDanXJW06+pabClZzgKkTLLIcngHp4tiFrFp+7Dk2UhLgV8p3chhdaYRbiqykgb6FmaPp9n/Kj1lyyASc9+xJW/K4fJz37ki/GHODve83PY1nl0BrGjWyMuFSenQ7v/Et0PRdHMuMsdNlJhMB4KITEbIt6hFUKh1wyDMMwVUetKZRVG+VU3CwXrq5ZIvSQbG5B4qgW98W9nWAXsmbyuevQRBtVUb9EexwXkDcJ73MrRlIpeH6vqbxZg0sWAWRUEC3mHWlVc7HA1LgpeKuLVJ41hHdGwkq9QkAJYbYIXXbSTgCmyq/VDBt0DMMwTNVRawpl1Ui5FDftCDK30vE1O8jtqYQixkUZECVQFXVcQN7E41lRCw4uQgY9vdd0obVtmSxSUeM0v5h3pOscWrsxYtEn0kWGUAiJuS3T99+FIS+jbVaL5efVCBt0DMMwTNVRrSvwTLC4Em8IGtWkNrGpC+O6ibpfIh1eKPvCiMXEXmM8F4wVNTbS9hWx4GBR5kFm8Hh6r+nUJXsODCO+QFvMvdh3pN5ANsuhLcbw1PeJ6SKD2qvm0JBvntWMsclDmBTTIZZ1FEXP6uvs21llsCgKwzAMU5WwyiWjx5VwSQkJWqSjWMoqLiQRa0k2tyDRthiDkyPGZ9qFp6tisBCRMSuaXfR7TSLEk2xsUNRNo1G0NbbhlLoObBnagcEQ0JYDepaeje41X3F9WZ7GjU2fmD7Dkxn07hlwJ6iC6v6dcCOKwgYdwzAMwzA1QaUaTqU2NN1MYss24dVN7JONDVKPUlUr1zowsnzrbxtDKbn5esRfexTjoem8urqcQPztxRt1fhmeCooCrNRYFALxt/YrJTiqwZD3CTcGnaeQSyK6D8CZAIaEECsln18A4Nr8n4cAfFYI8Rsv52QYhmEYhpFR9hBCE0oZIuw27LRsoYm6PKjE3BaNMQdURliqJ3Q1ArVGq/A3JFgixKMOS03sehTjYa1IyniIkNj1aFEGXdHjxq5uYiXlP1YRXssWfBvAhy0+fw3AqUKITgBfAXCPx/MxDMMwDMNMMS1x3omxyTFEQ1HN55WQW+mnpL8dVVPSQ5cHZaY8WNXKtToJfyuj1TM25SsGTWb8U9v7HlG8fPEW5V+T0gCesSh9UUBdIqX3nF425hzgyUMnhNhCREssPv+F6s/tAAIowMIwDMMwzExE741KH0kjQhG0zG5BeiJdUav7pfKEVU1JD51HyU9VxopBp3YauNFqoS7ZlgNSktO35eBavMVzG4Gy5UNWc06dFaVUubwEwNOyD4joUgCXAsAxxxxTwiYxDMMwDFOtyLxRGZFBfaQeP//Ez8vUqtKjnqQSEWT6CBVnGOkm9j0TYcRnRTEuJqd2qQTvqmdURlabSS4lEaFTUp/QT+OjZ+nZ0hy6nqVnGxQyARjrvfkpSlOC0hcyKkoF12dKYtAR0QehGHQnyz4XQtyDfDjmqlWrKkulhWEYhmGYiqRqvFEBop+kyoy5ijWMVBP7bgCoFu9JkcaNWZ20QgkAtYEBwFfjo5Anl9j1qFHlcvM35V8q5Dm69OC5NURLJeJjFY5ckePMBZ5VLvMhl0/KRFHyn3cCeBTA6UKIP9gdj1UuGYZhGIZxQqWWKSglZn0QohCEEJVtGFUjknILbqT09d5UWT239sZ2ACjd2N64EsnMPkV9MxJGWyaLngPD6I7MV8oruCi/4KSkgboPmmc1YywzhsncpOn+bo5tRaWq4JrhRuXSqyiKXUOOAfADABc6MeYYhmEYhmGc0vPeHtSF6zTbKtYbFRBm3kghBItKBIEkPDE5i9C140Z0PtCJrk1dSO5Kmn5dLfhh5lQZHB0sqfc5edzZiC+Yj1Q0AkGEVDSC+IL5SB53trKDTpF0Csl2My/YF7d+EZ0PdOLkh07G9duuR2o0BQGB9JG0xpgr7C8TivEq+GMWdlxx4chF4MmgI6KHAPwSwLuJaA8RXUJElxPR5fldNgCYD+AuIvo1EbHrjWEYhmEYXyilemSlUsuT1IpEZ8QUShGkwgShKkVgZdQVsLp3pbyvib3PanLrgHxJg73PKn/ETDQNKWRQxTQzOHMiZ2rAyZAdx6uRW8sLQF5VLs+z+fwzAD7j5RwMwzAMwzBmlK2OWoVQyhp3DAx11LzUz7O7d6W6r7aGkqzGHQCIrPKvKqfOrBakW2SGq9c6k7Vc466UKpcMwzAMwzCMj9TyJLUi0Rk3XkoROLl3pbivtoaSvtQAhaaNuQJ5Vcyes26XCr+4wcxwlRnAEYrgcOawVCVURq0uAHkWRfEbFkVhGIZhGIZhKhaVymXXMYuQCpNhl2oS5pGKjVAU8YNH0P2WRMkz3gJIxEUAAuLDjoRf1EQogqZZTY5qRxYrqFKNuBFFYYOOYRiGYRiGYYrAq/JipaApBxBtRs/gG+geGZ7eQa3k6VH10taAc1gWotZVbt0YdBxyyTAMwzAMw8xYvNQ2K2vIq4/FvjWhiBtXAmpjDtAWGpfl1EXrle2S4wIu+sdFzTuuQzkNG3QMwzAMwzAzBC/GSy2i9yAVU8C7LHlZLot9u8KuTIE+p87GmHTVP5KyEBpjUoVXkZRaItA6dAzDMAzDMExlUDBeCjXA3Ejs1ypea5uVDSvDxytmZQrU2zvPVcIr48PKv16NyAIuat7VchkCt7BBxzAMwzAMMwOoWuMlQKo2bM+F4eOatRuUEEo1JiGVvuPEmMzDdSin4ZBLhmEYhmGYGUDVGi8B4nfYXslCWnX18DTbHWDZTpchlb7iIj8PqN0yBG5hg45hGIZhGGYGwDlHRvwszO5HPp5j1m5A8sdXI9HcgMFIGG2ZLHpGxtDtwIvmqJ2d55bGgNNTTmOyiuGQS4ZhGIZhmBkA5xwZ8TNsr5QhrcmmRsQXzEcqGoEgQioaQXzBfCSbGiuqnUURVH5eDcMeOoZhGIZhmBlAWSX2Kxi/wvZKGdKaeCGBcTGp2TYuJpF4IWF7LRx6W3uwQccwDMMwDDND4Jyj4Ag6pFWd9yYgpPs4Mco49Lb24JBLhmEYhmEYhvFIkCGt+pITZjgxyqop9Da5K4muTV3ofKATXZu6ZnSJDSvYQ8cwDMMwDMMwHgkypFWW96bHqVFWLaG3JRWZqXJICHMrvxysWrVK7Nixo9zNYBiGYRiGYZiKoPOBTlPPHIEq1ijzQtemLmloaHtjO3rP6S1Di0oLET0vhFjlZF/20DEMwzAMwzBMBWOW91bLxg2LtziHc+gYhmEYhmEYpoKpprw3vzDLB2TxFiNs0DEMwzAMwzBMBeNnvbxqYSYascXCIZcMwzAMwzAMU+HMtJIT1SLeUgl4MuiI6D4AZwIYEkKslHxOABIAzgAwBuBTQogXvJyTYRiGYRiGYRgt6jp1tWL8zDQjtli8hlx+G8CHLT4/HcCy/H+XAvgPj+djGIZhGIZhGEaFvk5dQeKf67bNDDwZdEKILQD2W+xyFoD/FgrbAbQQUbuXczIMwzAMwzAMM42sTt14dhyJFxJlahFTSoIWRTkawBuqv/fkt2kgokuJaAcR7XjrrbcCbhLDMAzDMAzD1A4s8T+zCdqgI8k2Q1VEIcQ9QohVQohVCxcuDLhJDMMwDMMwDFM7sMT/zCZog24PgMWqvxcBGAj4nAzDMAzDMAwzY2CJ/5lN0Abd4wD+nhRWA0gLIYxl7hmGYRiGYRiGKYqZWKeOmcZr2YKHAKwBsICI9gC4AUAUAIQQdwN4CkrJglehlC242Mv5GIZhGIZhGIYxwhL/MxdPBp0Q4jybzwWAz3k5B8MwDMMwDMMwDCMn6JBLhmEYhmEYhmEYJiDYoGMYhmEYhmEYhqlS2KBjGIZhGIZhGIapUtigYxiGYRiGYRiGqVLYoGMYhmEYhmEYhqlSSBGirByI6C0Ar5e7HRIWANhb7kbMYLj/ywv3f/ngvi8v3P/lg/u+vHD/lxfu//JRKX3/NiHEQic7VpxBV6kQ0Q4hxKpyt2Omwv1fXrj/ywf3fXnh/i8f3Pflhfu/vHD/l49q7HsOuWQYhmEYhmEYhqlS2KBjGIZhGIZhGIapUtigc8495W7ADIf7v7xw/5cP7vvywv1fPrjvywv3f3nh/i8fVdf3nEPHMAzDMAzDMAxTpbCHjmEYhmEYhmEYpkphg45hGIZhGIZhGKZKYYPOAUT0YSL6PRG9SkRfKHd7ahkiWkxEPyWifiJ6mYh68tvjRPQmEf06/98Z5W5rrUJEu4not/l+3pHfNo+I/h8R7cz/O7fc7axFiOjdqjH+ayIaIaIrefwHBxHdR0RDRPSSapt0vJPCN/K/BX1E9N7ytbz6Men7rxHR7/L9+ygRteS3LyGiw6pn4O7ytbw2MOl/03cNEV2XH/u/J6IPlafVtYFJ339P1e+7iejX+e089n3GYq5Zte9+zqGzgYjCAP4A4P8A2APgOQDnCSFeKWvDahQiagfQLoR4gYjmAHgewEcBnAvgkBDi38rawBkAEe0GsEoIsVe17asA9gshbssvaswVQlxbrjbOBPLvnjcBvB/AxeDxHwhEdAqAQwD+WwixMr9NOt7zk9vPAzgDyn1JCCHeX662Vzsmfd8F4CdCiAwR3Q4A+b5fAuDJwn6Md0z6Pw7Ju4aIjgXwEIATAHQA+DGAdwkhsiVtdI0g63vd5/8OIC2EuJHHvv9YzDU/hSp997OHzp4TALwqhNglhDgC4GEAZ5W5TTWLECIlhHgh//8HAfQDOLq8rWKgjPkH8v//AJQXHxMsawH8UQjxerkbUssIIbYA2K/bbDbez4IyARNCiO0AWvITA6YIZH0vhOgVQmTyf24HsKjkDZshmIx9M84C8LAQYkII8RqAV6HMj5gisOp7IiIoi9gPlbRRMwiLuWbVvvvZoLPnaOmxL9YAACAASURBVABvqP7eAzYwSkJ+Veo4AM/mN/1j3tV9H4f8BYoA0EtEzxPRpfltRwkhUoDyIgTQWrbWzRw+Ae0POo//0mE23vn3oLR8GsDTqr/fTkQvEtHPiOgD5WrUDED2ruGxXzo+AODPQoidqm089gNCN9es2nc/G3T2kGQbx6kGDBE1Afg+gCuFECMA/gPAOwD8FYAUgH8vY/NqnZOEEO8FcDqAz+VDQ5gSQkSzAHwEwP/Nb+LxXxnw70GJIKL1ADIAvpvflAJwjBDiOAD/DOBBImouV/tqGLN3DY/90nEetIt5PPYDQjLXNN1Vsq2ixj8bdPbsAbBY9fciAANlasuMgIiiUB6w7wohfgAAQog/CyGyQogcgP8Eh3oEhhBiIP/vEIBHofT1nwvhBfl/h8rXwhnB6QBeEEL8GeDxXwbMxjv/HpQAIroIwJkALhD5RP98qN++/P8/D+CPAN5VvlbWJhbvGh77JYCIIgA+BuB7hW089oNBNtdEFb/72aCz5zkAy4jo7flV808AeLzMbapZ8rHj9wLoF0J8XbVdHat8NoCX9N9lvENEjfkEYRBRI4AuKH39OICL8rtdBOCx8rRwxqBZoeXxX3LMxvvjAP4+r3i2GopoQaocDaxViOjDAK4F8BEhxJhq+8K8UBCIaCmAZQB2laeVtYvFu+ZxAJ8gotlE9HYo/f+rUrdvBvA3AH4nhNhT2MBj33/M5pqo4nd/pNwNqHTySlv/COBHAMIA7hNCvFzmZtUyJwG4EMBvC5K9AL4I4Dwi+isoLu7dAC4rT/NqnqMAPKq86xAB8KAQ4n+J6DkAjxDRJQD+BOBvy9jGmoaIGqCo6qrH+Fd5/AcDET0EYA2ABUS0B8ANAG6DfLw/BUXl7FUAY1DUR5kiMen76wDMBvD/8u+h7UKIywGcAuBGIsoAyAK4XAjhVNCDkWDS/2tk7xohxMtE9AiAV6CEwn6OFS6LR9b3Qoh7YcydBnjsB4HZXLNq3/1ctoBhGIZhGIZhGKZK4ZBLhmEYhmEYhmGYKoUNOoZhGIZhGIZhmCqFDTqGYRiGYRiGYZgqhQ06hmEYhmEYhmGYKoUNOoZhGIZhGIZhmCqFDTqGYRim6iGiQ/l/lxDR+T4f+4u6v3/h5/EZhmEYxgts0DEMwzC1xBIArgy6QtFeCzQGnRDiRJdtYhiGYZjAYIOOYRiGqSVuA/ABIvo1EV1FRGEi+hoRPUdEfUR0GQAQ0Roi+ikRPQjgt/ltPySi54noZSK6NL/tNgD1+eN9N7+t4A2k/LFfIqLfEtHfqY69mYg2EdHviOi7lK+SzTAMwzB+Eyl3AxiGYRjGR74A4F+FEGcCQN4wSwshjiei2QC2EVFvft8TAKwUQryW//vTQoj9RFQP4Dki+r4Q4gtE9I9CiL+SnOtjAP4KwHsALMh/Z0v+s+MArAAwAGAbgJMAbPX/chmGYZiZDnvoGIZhmFqmC8DfE9GvATwLYD6AZfnPfqUy5gDgn4joNwC2A1is2s+MkwE8JITICiH+DOBnAI5XHXuPECIH4NdQQkEZhmEYxnfYQ8cwDMPUMgTg80KIH2k2Eq0BMKr7+28A/LUQYoyINgOoc3BsMyZU/58F/94yDMMwAcEeOoZhGKaWOAhgjurvHwH4LBFFAYCI3kVEjZLvxQAcyBtzfwFgteqzycL3dWwB8Hf5PL2FAE4B8CtfroJhGIZhHMIrhgzDMEwt0Qcgkw+d/DaABJRwxxfywiRvAfio5Hv/C+ByIuoD8HsoYZcF7gHQR0QvCCEuUG1/FMBfA/gNAAHgGiHEYN4gZBiGYZiSQEKIcreBYRiGYRiGYRiGKQIOuWQYhmEYhmEYhqlS2KBjGIZhGIZhGIapUtigYxiGYSqGvMDIISI6xs99GYZhGKZW4Rw6hmEYpmiI6JDqzwYocv3Z/N+XCSG+W/pWMQzDMMzMgQ06hmEYxheIaDeAzwghfmyxT0QIkSldq6oT7ieGYRjGKRxyyTAMwwQGEd1ERN8jooeI6CCATxLRXxPRdiIaJqIUEX1DVScuQkSCiJbk//5O/vOnieggEf2SiN7udt/856cT0R+IKE1E3ySibUT0KZN2m7Yx//lfEtGPiWg/EQ0S0TWqNl1PRH8kohEi2kFEHUT0TiISunNsLZyfiD5DRFvy59kP4EtEtIyIfkpE+4hoLxH9DxHFVN9/GxH9kIjeyn+eIKK6fJuXq/ZrJ6IxIppf/J1kGIZhKhU26BiGYZigORvAg1CKd38PQAZAD4AFAE4C8GEAl1l8/3wA/z979x0dd3Xn//95p0hT1LtcJFm25Cq5CcmUBAMBO7HphASWQGCTkGwSDGyyfNkE4/DdL0vaAskvkJBCSDbJQggnwTFsCgmHFrDlJnfJli1btmwVq2tGmnJ/f3ymqriojSS/H+f4zGjmzsydsRF66b7v+z4CpAFHgf97vmOVUlnAS8BXA697GCg/w/MMOcdAqPorsBHIBYqBNwOP+ypwS2B8CvAZwH2G14l0CbAPyAS+CSjgPwKvsQAoDLw3lFIWYBNwEOOcvZnAS1prd+B93tHvM/mT1rrlHOchhBBiEpFAJ4QQYqy9o7XeqLX2a61dWustWusPtNZerXUtxsHdl5/h8S9rrSu11h7gV8CSYYxdC+zQWv8hcN+TQPNQT3KWOV4HHNNaP6217tVad2itNwfu+wzw71rrmsD73aG1Pn3mjyfkqNb6Wa21L/A5VWut39Ba92mtGwNzDs7hYoyw+ZDWujsw/t3AfS8AtwcOUgf4FPDLc5yDEEKIScYS6wkIIYSY8o5FfqGUmgd8F1iO0UjFAnxwhsefjLjeAyQMY+y0yHlorbVSqn6oJznLHGdirIwNZiZw6AzzO5P+n1MO8D2MFcJEjF/CNkW8zhGttY9+tNbvKqW8wGVKqVYgD2M1TwghxBQkK3RCCCHGWv/uWz8CdgNztNZJwHqM8sKx1ADMCH4RWL2afobxZ5rjMWD2EI8b6r7uwOs6Im7L6Tem/+f0TYyuoSWBOXy63xzylVLmIebxC4yyy09hlGL2DjFOCCHEJCeBTgghxHhLBNqB7kDzjjPtnxstfwSWKaWuDew/W4exV204c3wVyFNKfUkpFaeUSlJKBffj/QT4D6XUbGVYopRKw1g5PInRFMaslPockH+WOSdiBMF2pdRM4CsR9/0DaAEeV0o5lFJ2pdSlEff/EmMv3+0Y4U4IIcQUJYFOCCHEePtX4C6gE2Ml7MWxfkGt9SngE8B/YQSh2cB2jBWw85qj1roduBq4GWgEqgnvbfs28HvgDaADY++dTRtnBH0W+HeMvXtzOHOZKcCjGI1b2jFC5O8i5uDF2Bc4H2O17ihGgAvefwTYBfRprd87y+sIIYSYxOQcOiGEEBecQKniCeAWrfXbsZ7PWFBK/QKo1VpviPVchBBCjB1piiKEEOKCoJRajVGq6AYexjiaYPMZHzRJKaUKgeuBkljPRQghxNiSkkshhBAXisuAWoySx9XADVOxWYhS6j+BncDjWuujsZ6PEEKIsSUll0IIIYQQQggxSckKnRBCCCGEEEJMUhNuD11GRoYuKCiI9TSEEEIIIYQQIia2bt3arLU+0/E6IRMu0BUUFFBZWRnraQghhBBCCCFETCil6s51rJRcCiGEEEIIIcQkJYFOCCGEEEIIISYpCXRCCCGEEEIIMUlNuD10QoipxePxUF9fj9vtjvVUhBBiyrLZbMyYMQOr1RrrqQghxpkEOiHEmKqvrycxMZGCggKUUrGejhBCTDlaa1paWqivr2fWrFmxno4QYpxJyaUQYky53W7S09MlzAkhxBhRSpGeni6VEEJcoEYU6JRSq5VSB5RSB5VS/2eIMbcqpfYqpfYopX49ktcTQkxOEuaEEGJsyfdZIc7PptpNXPPyNZS+UMo1L1/DptpNsZ7SsA275FIpZQZ+AFwN1ANblFKvaq33RowpAh4GLtVatyqlskY6YSGEEEIIIYQYrk21m9jw3gbcPmNVu6G7gQ3vbQBgTeGaGM5seEayQlcOHNRa12qt+4D/Aa7vN+azwA+01q0AWuvGEbyeEEKMq4KCApqbm2M9DSHGzc9//nO+9KUvxXoaQggxpp7e9nQozAW5fW6e3vZ0jGY0MiMJdNOBYxFf1wdui1QMFCul3lVKva+UWj3YEymlPqeUqlRKVTY1NY1gSkKIye73249z6RN/Y9b/2cSlT/yN328/HuspxUbVS/DkItiQYlxWvRSzqUzGYLtjxw5ee+21WE9j2KZSKdB4ad+4kZorr2Lf/AXUXHkV7Rs3jtpza63x+/2j9nyD8fl8Y/r8QlzIPD4P2xu38+OqH3PvX+6lobth0HEnu0+O88xGx0gC3WDF2rrf1xagCFgJ3Ab8RCmVMuBBWj+ntS7TWpdlZmaOYEpCiMns99uP8/Aruzje5kIDx9tcPPzKrhGFuu7ubtasWcPixYtZtGgRL774Iq+99hrz5s3jsssu47777mPt2rUAtLS0cM0117B06VLuvfdetO7/LW2cVL0EG++D9mOANi433hfTUDfZTOZAFywFauhuQKNDpUCjEepuuOEGli9fzsKFC3nuuecAeP755ykuLubyyy/n3XffDY3duHEjFRUVLF26lI985COcOnUKgA0bNnDXXXdxzTXXUFBQwCuvvMK//du/UVJSwurVq/F4PCOe5/lq37iRhkfW4z1xArTGe+IEDY+sH1GoO3LkCPPnz+df/uVfWLZsGWazmYceeojly5fzkY98hM2bN7Ny5UoKCwt59dVXAdizZw/l5eUsWbKE0tJSampqOHLkCPPmzeOuu+6itLSUW265hZ6eHsD4Zcljjz3GZZddxm9/+1t27NjBihUrKC0t5cYbb6S1tRWAlStXcv/993PJJZewaNEiNm/ePPIPTYgprNfXy5aTW3h257N85k+f4ZLfXMKdr9/J97Z/jyZXEw6LY9DH5Thzxnmmo2MkxxbUAzMjvp4BnBhkzPtaaw9wWCl1ACPgbRnB6wohJqlvbNzD3hMdQ96//Wgbfb7o34K7PD7+7eUqfrP56KCPWTAtiUevXTjkc/7v//4v06ZNY9Mm44fh9vZ2Fi1axFtvvcWsWbO47bbbwvP7xje47LLLWL9+PZs2bQr9wDvqXv8/cHLX0PfXbwFfb/RtHhf84Uuw9YXBH5NTAh99Ysin7O7u5tZbb6W+vh6fz8cjjzxCYmIiDz74IBkZGSxbtoza2lr++Mc/0tLSwm233UZTUxPl5eVnDLZHjhxh9erVXHbZZbz//vssXryYu+++m0cffZTGxkZ+9atfUV5ezunTp7nnnnuora3F4XDw3HPPUVpayoYNGzh8+DANDQ1UV1fzX//1X7z//vu8/vrrTJ8+nY0bN2K1Wtm6dSsPPvggXV1dZGRk8POf/5zc3FxWrlxJRUUFf//732lra+OnP/0pFRUVrF+/HpfLxTvvvMPDDz/Mvn37SEhI4Ctf+QoAixYt4o9//CPAOc1/NH1z8zfZf3r/kPdXNVXR5++Lus3tc7P+3fW8XP3yoI+ZlzaPh8ofOutr/+xnPyMtLQ2Xy8VFF13EmjVrePTRR9m6dSvJyclcccUVLF26FCD0mSil+MlPfsK3vvUtvvvd7wJw6NAh/v73v7N3714uvvhifve73/Gtb32LG2+8kU2bNnHDDTec68dxTk4+/ji9+4b+zFw7d6L7oj8z7XbT8LWv0/bSbwd9TPz8eeT8+7+f8XUPHDjA888/zzPPPINSipUrV/LNb36TG2+8ka9//ev85S9/Ye/evdx1111cd911/PCHP2TdunX80z/9E319ffh8Pk6dOsWBAwf46U9/yqWXXso999zDM888E/q3aLPZeOeddwAoLS3l+9//Ppdffjnr16/nG9/4Bk899RRg/Df83nvv8dZbb3HPPfewe/fuc/78hJjqXF4XO5t2UnmykspTlexq2kWfvw+FYl7aPG4pvoWynDKWZy0nxZYyYA8dgM1sY92ydTF8F8M3khW6LUCRUmqWUioO+CTwar8xvweuAFBKZWCUYNaO4DWFEFNY/zB3ttvPRUlJCX/961956KGHePvttzl8+DCFhYWhs5oiA91bb73FHXfcAcCaNWtITU0d9uuOSP8wd7bbz0Ew2O7cuZPdu3ezevVq7r33Xl5//XXeeecdIsvdg8F2+/btXHfddRw9OniYDjp48CDr1q2jqqqK/fv38+tf/5p33nmH73znOzz++OMAPProoyxdupSqqioef/xx7rzzztDjDx06xKZNm/jDH/7AHXfcwRVXXMGuXbuw2+1s2rQJj8fDl7/8ZV5++WW2bt3KPffcw9e+9rXQ471eL5s3b+app57iG9/4BnFxcTz22GN84hOfYMeOHXziE58Y8fzHU/8wd7bbz8f3vvc9Fi9ezIoVKzh27Bi//OUvWblyJZmZmcTFxUV9VvX19axatYqSkhK+/e1vs2fPntB9H/3oR7FarZSUlODz+Vi92thRUVJSwpEjR0Y8z/PVP8yd7fZzlZ+fz4oVKwCIi4uLep+XX3556DMIvueLL76Yxx9/nG9+85vU1dVht9sBmDlzJpdeeikAd9xxRyjAAaHPvL29nba2Ni6//HIA7rrrLt56663QuOD3qg9/+MN0dHTQ1tY2ovcmxGTW4+nh3ePv8vS2p/nUa5/ikt9cwmf//Fl+vOvHuL1ubpt3G9+/8vu8/cm3eenal3io/CGuyruKFJtRKLimcA0bLtlArjMXhSLXmcuGSzZMyoYoMIIVOq21Vyn1JeBPgBn4mdZ6j1LqMaBSa/1q4L5rlFJ7AR/wVa11y2hMXAgx+ZxpJQ3g0if+xvE214Dbp6fYefHei4f1msXFxWzdupXXXnuNhx9+mKuvvvqM48el9fcZVtIAY89c+7GBtyfPhLuHV3ZXUlLCV77yFR566CHWrl1LYmLigGAbXJF86623eOWVV4BzC7azZs2ipKQEgIULF3LVVVehlIr6Qfedd97hd7/7HQBXXnklLS0ttLe3A2cPBwcOHGD37t2hvzufz0dubm7o9W+66SYAli9fPqwwcS7zH01nW0m75uVrBt3fkevM5fnVzw/7dd98803++te/8o9//AOHw8HKlSuZN28e+/btG3T8l7/8ZR588EGuu+463nzzTTZs2BC6Lz4+HgCTyYTVag39d2MymfB6vcOe41DOtpJWc+VVRrllP5Zp08j/5S+G/bpOpzN0vf/7jPwMgu/59ttvp6Kigk2bNrFq1Sp+8pOfUFhYOOD7SuTXka9xJmd6DiGmus6+TrY3bg+twO1t2YtP+7AoCwsyFnDngjspyy5jadZSEuISzuk51xSumbQBrr+RlFyitX4NeK3fbesjrmvgwcAfIYQ4o6+umsvDr+zC5Qk3B7BbzXx11dxhP+eJEydIS0vjjjvuICEhgWeffZba2lqOHDlCQUEBL774Ymjshz/8YX71q1/x9a9/nddffz20f2XcXbXe2DPniQi3Vrtx+zCNZbAN/mALQ/+gO1jZZvA1zhYOtNYsXLiQf/zjH2d8fbPZPGSYsFgsUU0tIg9gPpf5j6d1y9aNSSlQe3s7qampOBwO9u/fz/vvv4/L5eLNN9+kpaWFpKQkfvvb37J48eLQ+OnTjV5nL7wwRKnvBJH1wP00PLIeHfH3qmw2sh64f1znUVtbS2FhIffddx+1tbVUVVVRWFjI0aNH+cc//sHFF1/Mb37zGy677LIBj01OTiY1NZW3336bD33oQ/zyl78MrdYBvPjii1xxxRW88847JCcnk5ycPJ5vTYhx1d7bztZTW6k8VUnlyUoOtB7Ar/1YTVZKMkq4Z9E9lOWUsSRzCQ7r4PvhLiQjCnRCCDGablhq/PD47T8d4ESbi2kpdr66am7o9uHYtWsXX/3qV0Nh4dlnn6WhoYHVq1eTkZERtT/q0Ucf5bbbbmPZsmVcfvnl5OXljfg9DUvprcblG49Bez0kzzDCXPD2YYh1sA0+5yOPPMKbb75JRkYGSUlJ5/TYuXPn0tTUFPqB2OPxUF1dzcKFQ6/4JiYm0tnZGfq6oKAgtGdu27ZtHD58eGRvaAwFf2P89LanOdl9khxnDuuWrRvxb5JXr17ND3/4Q0pLS5k7dy4rVqwgNzeXDRs2cPHFF5Obm8uyZctC3RY3bNjAxz/+caZPn86KFSsm9GeWfO21ADQ++RTehgYsublkPXB/6Pbx8uKLL/Lf//3fWK1WcnJyWL9+PR0dHcyfP58XXniBe++9l6KiIr7whS8M+vgXXniBz3/+8/T09FBYWMjzz4dXZFNTU7nkkkvo6OjgZz/72Xi9JSHGxWn3aSPABVbgalpr0GjizfGUZpZyb+m9lGWXUZpZis1ii/V0JxwJdEKICeWGpdNHFOD6W7VqFatWrYq6rauri/3796O15otf/CJlZWUApKen8+c//zk07sknnxy1eZy30ltHFOD6i3Ww3bBhA3fffTelpaU4HI7zWvGJi4vj5Zdf5r777qO9vR2v18v9999/xkB3xRVX8MQTT7BkyRIefvhhbr75Zn7xi1+wZMkSLrroIoqLi0f8nsbSWJQCxcfH8/rrrw+4feXKldx9990Dbr/++uu5/vr+x8sSVXoJxn9PQ903npKvvXZUA1xBQUFU45Ezvc/gfQ8//DAPP/xw1H0dHR2YTCZ++MMfDniN/iW9S5Ys4f333x90PjfffDP/+Z//eT5vQYgJq9nVHFp923pqKwfbDgJgt9hZnLmYLy75ImU5ZZRklBBnjovxbCc+FbO23EMoKyvTlZWVsZ6GEGKU7Nu3j/nz58d6GlGefPJJXnjhBfr6+li6dCk//vGPcTguvJKNrq4uEhISQsG2qKiIBx54INbTEmJKOXLkCGvXrh1RV8qVK1fyne98J/TLp6FMxO+3QgCc6j5lBLhAiDvScQQAh8XB0uyllGWXUZZdxsL0hVjN1thOdoJQSm3VWp/5P/rgWAl0QoixJD9gTFwSbIWYWuT7rZgoTnSdCIW3ylOVHOs0Gn0lWhNZlr3MCHA5ZcxLm4fFJAWDgzmfQCefoBBCXKAeeOCBc16Ra2lp4aqrrhpw+xtvvEF6evpoT00IIcQkobWmvrM+agXuRLfRdTY5PpllWcu4bd5tlGWXUZxajNlkjvGMA6peGtW96rEkgU4IMea01tJie5JLT09nx44dsZ6GEGIIE63iSkxdWmuOdByJWoFr7GkEIM2WxvLs5dy50DhGoCi1CJMaybHXY6Tqpehu0u3HjK9hUoY6CXRCiDFls9loaWkhPT1dQp0QQowBrTUtLS3YbNL9T4w+rTWH2g6FVuC2ntpKs6sZgAx7Rmj/W1lOGYXJA89djBmtwdUKXY3Q3Whcdp0yLjc/F300EBhfv/GYBDohhOhvxowZ1NfX09TUFOupCCHElGWz2ZgxY0aspyGmAL/2U9NaE9WFsrXXOL4m25FNRW5FKMTlJ+WPb4DTGno7I0JaIKBFhrVQeGsEv2fgc5isg98ORvnlJCSBTggxpqxWK7NmzYr1NIQQQggxCJ/fx4HWA6Hyya2nttLR1wHA9ITpfGjGh0IrcDMSZoxNgOvrMQJZd1MgmJ2CrqZ+IS1w3ese+HhlAmcmJGRBQjZkzg9fT8gKX3dmgj0Vnioxyiz7S56cvxSRQCeEEEIIIcQFwuv3sq9lX6iEcvup7XR6OgHIS8zjI/kfCa3A5SbkjuCFegcpd4wIbKHw1gR9nYM/hyM9HMpmrhgipGWBIw3Op9nKVeuj99ABWO3G7ZOQBDohhBBCCCGmKI/Pw56WPaESyu2N2+nx9gBQkFTA6lmrKcsuY3n2crKd2Wd+Mp/XCGL996QNtk/N3Tb4c9iSA6EsG3KXDAxoCVlGSHNmwFidSRfcJzdFulzKOXRCCCGEEEJMEb2+XnY17QqtwO1s3InbZ5QpzkmZw/Ls5ZTlGCtwGfYM8PuhpyWirHGwVbRAWOtpAQbJDnGJkJAZLmsMBrbgbcGQlpAFlvjx/UAmKTmHTgghhBBCiAuAy+uiqqkqtAJX1VRFn78PhaI4MZ+bs1dQFp/FcpODVFcHnDwBB38MXf9hhLfuJtC+gU9ssYVXzVJnwcyKfiEtO7xvLc45/m9chEigE0IIIYQQYqLTGvq66GmrY8eJ96ls3E5lew27XKfw4scEzPOb+WSvl7KuNpb1dJHsrwPeCj+HyRoub0ycNnjJY3AlLT4RJsoRBOKMJNAJIYQQQggRK3090a32+7Xf7+pqYJu7iUrdw9Y4M3vj4/AqhVlrFvb28anePsqwsTQunURnNqRkw+xB9qQlZBkdHiWkTTkS6IQQQgghhBhN3r5B9qQNdnZa44AOj+0mxTabjcqEFCpt8ey3+vFbwYKTElsWdyfPpixjMUtyynGk5J9/h0cx5UigE0IIIYQQ4mx8XuhpPnvjkK5TZ+/w6MyC3MWQkE2rPZFt9FLZ18yWzjqqu46h0cSZ4ijNLOVzgQYmpZml2C328X3PYlKQQCeEEEIIIS5Mfj+4Tg/Sfr/fKlrXqTN0eEwIlzVmzoVZHx58T5ozE6w2ml3NbD21NXSQ98HGgwDYzDYWZy7mC3PWhgJcvFk6Qoqzk0AnhBBCCCEmnPYffI3G51/B26WxJCiy7r6J5C/+v7M/UGtjhexsq2hdjWfv8OjMgtQCmFk+eEg7hw6PjT2NRnjbZwS4w+2HAbBb7CzNWsrHZn2MspwyFqUvwjpW566JKU0CnRBCCCGEmFDaf/A1Gp75HdqnAIW3Cxqe+R24O0i+5fZ+IW2QfWq+voFParKEW+0n5gZKHvs3DskecYfHhq6G0BlwlScrOdp5FIAEawJLs5Zyw5wbKMsuY376fKwmCXBi5CTQCSGEEEKI2NPaCGTNB2j82ctonyn6bp/i1At/Qu15CRQok0aZFNiTUY5UlDMVlbAYpqWhEjJQiZmQkIFKzEIlZYMziKDYogAAIABJREFUDWW1osxmsFhRFnPgugU1zPCmtaa+qz5UPrn11FaOdx0HICkuiWXZy7h17q2U5ZQxL3UeZmleIsaABDohhBBCCDF+/D5oq4Omamg+AE3V+E8eoLfmIK6GXlwtcXi7B2/+4eszc/y9tEHu6Qj8qRvenMwR4c5iCVw3oyxGAFRmM1gtKLOFPnx0azed/h46vF248OAzQanFyqX2FFKcRaQ6M0iypxrPZalFWepoMgee22IOvJ4FZbWEr1ssxmtGjTMulSUwLjC/Qa8HLhnsutkMwTBrMg07wE4l7Rs30vjkU3gbGrDk5pL1wP0kX3ttrKc1LBLohBBCCCHE6PO4oeVgKLTRfACaa9BNNfS1eXGfjsPVYsXV5qT3tEL77IAdc2oSytw++NY2B+S9tBHt86G9XvB6B78euNReH/i8oeva5wVv4D5fYJyn//XgY3xor4dOVxstXU20dbfQ7jqNz9OH2Qc2ZSU5LoFpZicJyk4cFnD70F194D2Ky3c4/Dpe34B54hvkDY4XazioRga/qBBrCaxk9gu0/ccNuD4gkEaE2MDK6KCB1hp47v7XzxRozxRig9cHCa/tGzfS8Mh6tNsNgPfECRoeWQ8wKUOdBDohhBBCDMum2k08ve1pTnafJMeZw7pl61hTuCbW0xLjzdUGzdXQdCAivFUbq3Daj9dtwtUSh7snA1erA9fJHPw9HgCU3Y590SJSryvBXroYe2kJlpwcOp75esQeOoMya7L++Wbi58wZs7fi135qWmtC5ZNbT23ltPs0AFmOLMqyV1EWOEagIKlgRCtd2u83QuSAQOoDryd0XXs9gYDa77pvqEAbPW5goB3iev9AG7jePwTr3t7AdV/g8RHXBwnX2ucDj2e0/orOn8kUtUKpzGZ8HR1Gh9MI2u2m8cmnJNAJIYQQ4sKwqXYTG97bgNtn/Ia7obuBDe9tAJBQNxVpDZ0NgdBWHb5srjaakQT4dTxuXwGurmxcTTm4j7XjaQycyWaC+OIZJK0pwb64FFtJKfFzZhs/bPcT7GYZ3eXy5nPrcnkefH4f1a3VoQYmWxu30t7bDkCuM5dLp11KWU4ZF2VfxIzEGaNaqqhMJiNsWC+Mxig6EF6HXFWNDIGhVdYzBFqvN7ySGgit4ZXQM4fg1l//ZtA5ehsaxvlTGR1K60HO04ihsrIyXVlZGetpCCGEEGIQWmuaXE18fOPHQysXkewWOzcV3YTNbCPeEo/NbMNmsRlfm+PD1y3xQ94unf9iyOeF1iOBlbbI8FYDfZ3hcfHJ6PQi+vQMY9WtoRdX7Sl6a+tCpYTWadOwlZZiLwkEuAULMDkcsXlfAV6/l/2n94eamGw7tY1Oj/G+ZiTMCK2+leWUMT1hekznKsZOzZVX4T1xYsDtlmnTKPrbGzGY0UBKqa1a67JzGSsrdEIIIYQYlMvr4lDbIapbq6P+BFcwhnrMqwdfxe1z4/EPr8zKrMzYLIGgFwiEA8Jg5O2B2+wWO/HmeOLN4etne0y8OR6TMp19UlNNXw+01EQ0JgmEt5ZDEPn3lpgLGcWw5DY8plxczSbcRztw7TuEe/du/D3GD8WmxETsJSUkXHW1UTpZsghLZmaM3lyYx+9hT/Oe0DECOxp30O3pBqAgqYBrCq4JhbgcZ06MZyvGS9YD90ftoQNQNhtZD9wfw1kNnwQ6IYQQ4gLn136Odx0PBbaa1hpqWmuo66hDY1Ty2C12ilKLuDr/aopTi/nRzh/R4m4Z8Fy5zlz+fMufAaOcrdfXi9vnptfbi8vnotfbS6+vF5fXFbrP7TXuD1339bvudYeeo8fbw2n36ajnDY4NzvV8BUNgZPizm+2hwDdYUJw0q4/dLYHSyANRXSVpPxoeo0yQOgsy50Lxasici88+E/cpD679h3BXVuHauRlvY6Mx3mrFNm8eyTfcECqdjCvIN0oIY6zP18eu5l2hFbidTTtxeV0AzE6ezdrCtZRll7E8ezmZjtgHThEbwX1y0uVSCCGEEJNOR18HNa01A8Jbj7cHAIUiLymP4tRiPlb4MYpTiilOLWZ64vSolaykuKSoPXQANrONdcvWhb42m8w4TA4c1rEvs9Na4/F7QkExMkCeS1B0+9zhoBkROls9rZz0nRzwvF6/d1jztChLKCie6+qjzTLIuMjrpjhs7jZsHQ3Etx7D1noEW0st8c3VqJ6I0G2xQ8YcmFkOyz5lrLxlzkUnzqT38FFcVVW4/r4LV9Wv6TtUa+ybA6z5eTgqKkKlk/Hz5mGKjx+Nv7YRc3vdVDVVhVbgqpqq6PX1AlCcWsyNc26kLMcIcGm2wY47EBeq5GuvnbQBrj/ZQyeEEEJMQV6/l7qOulBoCwa4hu7wpv+kuCTmps2lOLWYopQiilOLmZ0y+5wD2IXc5TK4+jhgpTEQFAcLjMHbQ+Ew8voQK5jB5xn26qMyYzPHE2+xY7M6iTfHkdtuJv94HzOOusit6yLjWAcWj9Hxry/RRmdRDq6iGfTOy8M3t5C41LSoMtXxWn0c7N/XFTOvYEfTDqOByamt7GrehcfvwaRMzE2dGyqfXJ69nOT45FGdjxDj6Xz20EmgE0IIISa5ZldzVHCraa3hUNsh+vx9gLEqNCtlVlRwK04tJsuRJQcMT0S9nYEyyRpoOoBuOkBf8wHc7XX0aj9upXArRW9iDu6U6biTptOblI3LmUmvI41eizUUCP1t7dirj5N46BSptU1kHG7F3mXskfNYFMen2zgyw8KhaSYO5PipT+zDO9gBcOcgcvXxbGWqZ1t9rGqq4jf7fxP6NwzG6jGARmNWZuanzeeinIsoyyljadZSEuMSR/7ZCzFBSFMUIYQQYgrq9fVS21Y7oElJZLfJLHsWRalFrJi/gqJUI7wVJhdiNUvnyAlFa+huim7/H7zecTw8zmRBpc0mPnMe8Quuh4y5kFkM6UUQnxD1lP7eXnr378e1pQrXrmpcVTvx1AX2yilF3OxC7Ndchr20BHtpKfFFRZQO0jLf6/cOvtLYryR1sPLVwR7j9rrp9nZz2n160DHn/JGhcVqdfOfy77A0aylOq3NYH70QU40EOiGEEGKC0VpzsvvkgOBW11GHL7B6YjPbmJ0ym8tnXB5acStKLSLVlhrj2Ysofr9xwHb/0NZ0ANxt4XFWpxHUCi4L7W0jYy6kzYJBwrj2++mrPYyraifuql24du3CvX9/6ABnS1YW9sWlpNx8C/bSEmyLFmFOSBjwPIOxmCxYTJZxCUxaa/r8fUa4i9jnePOrNw86vsfTw2XTLxvzeQkxmYwo0CmlVgNPA2bgJ1rrJ/rd/2ng20DwV03/n9b6JyN5TSGEEGIq6fZ0D9qkJHg2FsD0hOkUpxaHOkwWpxYzM3EmZtPAA5lFjHh7oeVg+My2YDfJlhrwRqxCOTONoLbwxkBoC4S3pOlwhvJXb3Mzrqpd4QC3ezf+jg4ATA4HtkWLSP/0XdhKSrAvXow1O3us3/GoUEqFSjMj97zlOnOj9nsGydECQgw07ECnlDIDPwCuBuqBLUqpV7XWe/sNfVFr/aURzFEIIYSY9Hx+H8c6j0WtuNW01lDfVR8ak2BNCHeXjFh1k9KyCcTdHm7/31wdvt56BLQ/MEhBSp4R1Aovj1hxKwbH2Tst+nt6cO/di2tnFa5dRojzngiEG7OZ+OJikj76UWPlraSE+NmzUeapFe7XLVt31i6qQgjDSFboyoGDWutaAKXU/wDXA/0DnRBCCHFBaXW3Rq26VbdWc6jtUOiHU5MyUZBUwKKMRdxUdFMouOU6c6VJyUSgNXSejD67LRjeuk6Gx5njIH0O5JRCyceNwJZRbNwWd26dQrXPR+/BQ1Glk701NeAzSmut06djX7wY+6fuNALcggWY7PaxeNcTSrBb6oXaRVWI8zGSQDcdOBbxdT1QMci4m5VSHwaqgQe01scGGSOEEEJMOh6fh9r22nCHybZqak7X0OhqDI1Js6VRnFrMrXNvjWpSYrPYYjhzAYDPa+xvazoQEd4Cf3o7wuPik4ygNueqcGjLnAsp+WA+9x+ltNZ4T56MLp3cswfdY5wBaEpKwl5SQuKVVxilk6WlWNLTR/tdTxprCtdIgBPiHIwk0A32K8T+ZyBsBH6jte5VSn0eeAG4csATKfU54HMAeXl5I5iSEEIIMfq01jT2NIZLJduM1bfDbYfxauOAaavJypyUOayYtiK04lacWkyGPSPGsxd4XIF9bdXh8NZcY+x584Xb4pOQYzQmKf1EoESyyNjvlphzxv1tQ/F1duLevTuqdNLX1AyAslqJnz+flJtuCpVOxhUUyAqtEOK8jSTQ1QMzI76eAZyIHKC1bon48sfANwd7Iq31c8BzYJxDN4I5CSGEECPS4+nhUNuhUGgL/mnvbQ+NyXXmUpRaFNVhMi8pb9QPVhbnqef0wE6SzdXQdpTQ75yVCVILjKBWdLVxmVFshDd7yrBfWns8uA9U495VFQpwfbW1RvkmEFdQgPPii7GXLsZeWkL8vHmY4uJG/p6FEBe8kQS6LUCRUmoWRhfLTwK3Rw5QSuVqrYMtiq4D9o3g9YQQQohR49d+jncep7qtOupQ7qMdR9GBH/7tFjtFqUVR3SXnpMyJ6sYnxpnWxjlt/UNbc7VxrluQxWac1TajDJb8k7HyllEMabPBOrJyV601nmPHokon3Xv3ovuM1T5zWhr20lKS167BVlKKvWQR5mT5NyOEGBvDDnRaa69S6kvAnzCOLfiZ1nqPUuoxoFJr/Spwn1LqOsALnAY+PQpzFkIIIc5LR1/HgCYlNa01uLwuABSKvKQ8ilOLWVO4JhTepidMx6RMMZ79BcrngdOHA3vbIsNbDXi6w+NsKUZ5ZPHq8NltGUVGl8lROtbB29qKe9euqADnazPOkFM2G7aFC0m9/Xbsi0uxlZRinT5NSieFEONGaT2xKhzLysp0ZWVlrKchhBBiEvL6vdR11A04kPtkd7gzYXJ8ciiwBf8UJhfisJ5bV0Ixynq7jLPagt0kg+HtdC34veFxSdOj2/8HL52Zw9rfNhR/by/uvXuNABconfQcPWrcqRTxc+ZgKy0Jl07OmYOySqmtEGJ0KaW2aq3LzmXsiA4WF0IIIWKl2dUcVSoZPBrA4/cAYFEWZqXMYnn28vCZbilFZDmyZPVktFS9BG88Bu31kDwDrloPpbcOHKc19LT06yYZuOwIn8OHMkNaoRHW5q0Nh7aMIohPHPXpa7+fvsOHo0snDxwArxEkLTk52EtKSPn4LdhLF2NbuBBzgpwJKISYWCTQCSGEmNB6fb0cajs0oFzytPt0aEyWPYuitCIunnYxRSnhowGsZlk5GTNVL8HG+4wOkgDtx4yvu5uNANY/vLlaw4+1Oowx+ZcE9rbNNcJb6iywjF2jEE9jY3Tp5K7d+Lu6ADA5ndhKSki/++5w6WR21pjNRQghRosEOiGEEBOC1pqG7oYBwa2uow6fNg5ZtpltzEmZE9Vdsii1iFRbaoxnfwH664ZwmAvyuOBPD4e/dmQYK2wLrg+EtkB4S5oOprHdm+jv7sa1Z09U6aS3IdCnzWLBVlxM0to1odLJuFmzUObR2XMnhBDjSQKdEEKIcdfV18XBtoNRwa26tZouT1dozIyEGQM6TM5MnIl5lBpdiPPQ3QwndkDD9sDlTqPT5FDu/l9jxc2RNi7T014vvQcP4qqqwlVVhbtqF70HD4LfD4B15kwcS5diu+tOo3RywXxMNjnYXQgxNUigE0IIMWZ8fh9HO48O2Ot2vCscBhKsCQO6SxalFuG0yl6lmOhqDIS2QHA7sSN6n1vabJhxEfR2gLt94OOTZ0L+xWM2Pa013oaGQHgLlE7u2Yt2GauF5uRkbKWlJH7kI4HSyRIsaeMTLIUQIhYk0AkhhBgVre7WQZuUuH1uAEzKREFSASUZJdxcdHMovOU4c6RJSax0ngyHtoYdxmXnifD96XMgbwVMWwK5SyC3FGyB89T676EDsNqNxiijyNfRgWvXrqjSSV9zMwAqLg7b/Pmk3HIL9tIS7KWlWPPy5N+TEOKCIoFOCCHEefH4PNS21w4Ib02u8KHOabY0ilOLuXXureGjAVIKiTfHx3DmF7iOhnBoC152BY9zUEaTkoLLwuEtpwRsSUM/X7Cb5bl0uTxHuq8P94EDobJJV1UVfYcPh+6PmzWLhEsvDR0bYJtbjIobuyYqQggxGUigE0IIMSitNY09jQPOdDvSfgSvNtq6W01W5qTM4eJpF4dKJYtTi8mwZ8R49hcwraHjRER422lc7zpl3K9MRqOSwsuN4DYtEN6GcyxA6a3DDnBaazxHj0aVTvbu3Yf2GMdOmDMysJeWknz9ddhKSrCXlGBOOkPAFEKIC5QEOiGEmMI21W7i6W1Pc7L7JDnOHNYtW8eawjUDxvV4egYcDVDdWk1HX0doTK4zl+LUYq6YeUUouOUn5WMxyf9KYkZrY3Uscr9bww7oDqyWKpPRVXL2ldHhLW789yd6T5+OKpt0V1Xhazf24Cm7HdvCBaR+6lOh0klLbq6UTgohxDmQ/wsLIcQUtal2Exve2xDaw9bQ3cCG9zbQ4mphesL0cIfJthqOdhxFowFwWBwUpRaxqmBVKLgVpRaRFCerIzGltXHWW2TJZMMO48BuMA7lzpwHRdeEw1v2wjENb+0bN9L45FN4Gxqw5OaS9cD9JF97LX63G/fefaHDul1VVXjqA41VTCbi58wh8ZqrjZW3xYuJnz0bZZEfSYQQYjiU1jrWc4hSVlamKysrYz0NIYSY1Dx+D6teXhW1r60/hSI/KZ+i1KJQcCtOLWZ6wnRMamzPCBNnoTW01fULbzvBFThM3WSBzPkwbXGgWckSyFlkNCUZJ+0bN9LwyHq02x2+0WzGkp2Nt7ERvEZZriU3F3tJSajjpH3hQkxO6WAqhBBnopTaqrUuO5ex8uswIYSYpDx+Dye6TlDXUcexzmPUddRxtPMoRzuOcqLrROgw7sH8Zs1vKEwuxGF1jOOMxaC0htbD0fvdGnaCq9W432SBrPkwb02gYclSY+XNOvxz1LTXi7+7G393N76uLuN6Vzf+7uD1rsDtPfi7Im7rDt/mOX48dM5biM+Hr7mZ9H/+Z+ylJdhKSrBmZY3gwxFCCHE2EuiEEGIC8/g9HO88HgpqkZf9Q5vT6iQvMY+F6QtZXbCal6pfor134Dlhuc5cFmUsGs+3IYL8fiO8RZZMNuwMn+dmskL2Aph/XbjbZNYCsNrQfj/+nkDAOno8FLR8XV2BMBYIZF1d+EIBzQhiobGBEBc8s+1slM2GKSEBk9OB2ZmAyenEmpODKSEBz7Fjgz5GezxkPXD/aH1iQgghzkICnRBCxNhQoa2uo46G7oYhQ9tHZ32UvMQ88pPymZk4kzRbWlQTidkps6P20AHYzDbWLVs3ru/vguX3o1sOoY9swXe4Ev+x3fhPVOPv6cbvMeH3WfHZp+GPX4Lfko7fnITfH4ev0YX//U78Xf+Lv+vlcCjr6Tmnl1VWqxHCEowAZkpwYs7MIK6gIOo2k9OJOXJcILCZE5yh2860r61n61a8J04MuN2Smzvsj0wIIcT5k0AnhBDjwOPzcLzreFRYC5ZJ9g9tCdYE8pLyWJSxiI8VfuyMoe1Mgt0sz6XLpQjTWqN7e/uVHnafZRWsE39rE/62Znyd7UZZotuL3wvoyL8ve+BPUKfxx1yLKSEBszMcpswpKVinTzcCmTMYxBKiw1jotmAoc2Iap3PZsh64f8AeOmWzyeqcEEKMM2mKIoQQo2Sw0Ba8PlRoy0/MZ2bSTPKT8slLzCMvKY/U+FRp1z4Muq8vVFIYte8rMpCFSg8H3hZZlhhs6HFGCkzxFkxWjcnUh9niw2TxY4o3YUpMxpSWjSl9OubsAkzZs4zbhlgdU/Hxk/LvfKgul0IIIUbmfJqiSKATQojz4PF5qO+qDzchiSiRPNF9Ar8ON4mQ0HZ2oeYc/Ve8urui94gFm3NEBrKu6PCm+/rO6TVNDkdE6WHEipczIfp2pwOz04HJ14HJdRxTdx2mjhpMbQcwq26UWaOsduNct+B+t2lLjHPfzFIAI4QQYviky6UQQoxAMLQFw1r/8sjBQltJRglrCteQl5Q3oULbWKygRDXnGGoVrKdfSBtkFczf1RXd8v4MlN0eCF3hkkTrtGnhPWL99oFF3RZ56XCgTEMcyeDzQvOB6KMCjuwGT2DvmtUBOaWw6A7IDRwXkFEs4U0IIURMyf+FhBAXpPMJbYnWRPKS8ijNKGVt4doJF9qG0v+cMO+JEzR8/RE8jU04y8sHroL1W/EabBXsvJpzxMVFhSmz04klMxPTrFlRq2ORIc3kTBhYkuhwjP6h0z4PNO2PPirg5G7wBro/Wp2QWwrL7gqvvmUUgck8uvMQQgghRkhKLoUQU5bH5+FY1zGOdUSf0Xa08+iQoS0Y1oJNSPKT8kmJT5mwoW0offX1HL7lFvxtA48tOCOLJTpg9W/C4YhswuGI7pIY0ZjD7HSixqk5x1n5PNC4L/qogFN7wBtYHYxLNMJbsGQydwmkz5bwJoQQImak5FIIccEYTmgrzTRW2iZ7aIvkaWig+4MP6PlgMz0ffIBnkHbykWY8+8ygJYkqLm5Sfw54+6Bx78Dw5gvsr4tPMsolL/pMOMClzYahyjCFEEKICU4CnRBiwuvz9YXLI/sdrn2m0Hbt7GtDpZF5iXmTPrRF8jY10R0Ib92bP8BTdxQAc3IyjvJy0u65h5Yf/QhvU9OAx1qmTSPxiivGe8qjz9trhLXg4dwndhhhLhTeko2Vt4p7A+FtKaTOkvAmhBBiSpFAJ4SYEAYLbcF9bQNCW1wi+Yn5Uz60RfK2thqrb5s/oPuDzfQdOgSAKSEBx0UXkXrbbTgrKoifOzfU9MOcnDR1zgnzuCPCW2D1rXEf+D3G/bZkI7St+EJ45S11FkzBfwtCCCFEJAl0Qohx0+fro76zfkATkjOFtsWZi6NCW35iPsnxyVMytEXytbfTU1lplFG+/wG91dUAKIcDx/LlpNx4A46KFdgWzEeZB9/rFexmOenOCfO4jPB2YnsgvO2Epn0Yp3QD9lQjtF3ypXC3ydQCCW9CCCEuSNIURQgxqvqHtsgSyYbuBjTh7znB0BbZjCQY2lJsKTF8F+PP19VFT2VlaA+ce98+0BoVH49j+TIc5RU4KsqxL1qEslpjPd3R09cDp3aH97s17DRW3oKHsNvTos94y10CKXkS3oQQQkxp0hRFCDGmgqGtfxOSwUJbUlwS+Un5LMlawvVJ14eakOQl5l1woS2Sv6eHnm3bQ3vg3Lv3gM+HslqxL1lCxhe/iLOiHNvixZgmSrfIkerrhpO7wvvdGnYYRwcEV2YdGUZoK14dDm/JMyS8CSGEEGcggU4IMaheXy/HO49Hhba6zjqOdRw759CWn2SURwrw9/bi2r4jtAfOVVUFHg9YLNhLSkj/7GdwVlRgX7oUk80W6+mOXG9XILxFdJtsrg6HN2eWEdrmrQ2Et8WQNF3CmxBCCHGeJNAJcQHr9fUa5ZERK2xnC21Ls5dyfeL1Uee1SWgbSPf14dq1K7QHzrVjB7qvD0wmbAsXkn7XnTgqVuBYthST0xnr6Y5Mbyc0VPULbzUQ/PeTkGOEtgXXh0snE3MlvAkhhBCjQAKdEFNcMLRFNiEJhreT3SejQltyfDJ5iXkS2oZBe724d+8OHSXQs3072uUCpYifP4/U22/HUVGOo6wMc2JirKc7fO4Oo2Qy8qiAloOEwltirhHaFt0cEd5yYjplIYQQYiqTQCfEFHC+oS0/MZ9l2cvIT8xnZtLMUGMSCW3nTvt8uPftD+2Bc1Vuxd/dDUB8UREpN9+Mo6Ic50UXYU6ZpHsFXW1wsiq86nZiB5w+FL4/aboR2kpvNS5zF0NiduzmK4QQQlyAJNAJEWObajfx9LanOdl9khxnDuuWrWNN4ZoB43p9vRzrODagCcnRTglt40H7/fTW1BgB7oPN9GzZgr+jA4C4WbNIunYtzhUrcFx0EZb09BjPNkLVS/DGY9BebzQYuWq9EcD6c7VGNys5sQNaD4fvT55pBLYltwXC2xJIyBy/9yGEEEKIQcmxBULE0KbaTWx4bwNuX/jg53hzPLcW30qWIytqX9up7lODhrbIdv/BSwltI6e1pq+2lu733zeOEtiyBV9rKwDWmTON1beKFTjKy7FmZ8V4tkOoegk23mec6xZktcOqx41z2yKPCmg9Eh6TnAfTFkcfFeDMGO/ZCyGEEBes8zm2QAKdEDF0zcvX0NDdMOT9KfEpA8KahLaxobXGU1cX2gPXvWUzvqZmACzTcnGWV+CoqMBZUY512rQYz/YcPbnQWJk7k5T8gee8OdLGZ35CCCGEGJScQyfEJDFUmFMo3v7k2xLaxlhf/XGjgUngKAHvyZMAWDIzjdW3inKcFRVYZ85ETeSOjH09RmOS5mqju2Tw8kxh7lO/N0ooJbwJIYQQk9qIAp1SajXwNGAGfqK1fmKIcbcAvwUu0lrL8pu44GmteX7P80Pen+PMkTA3BjynThmrb+9/QM8HH+A5fhwAc1oajvJynBXlOCpWEDerYOIFOK2huykQ1iKDWzW0HSPUZRIFqfmQUWyUUfZ1Dnyu5Jkw+4pxnLwQQgghxsqwA51Sygz8ALgaqAe2KKVe1Vrv7TcuEbgP+GAkExViquj2dPPIu4/wl7q/sCh9EQfbDkbtobOZbaxbti6GM5w6vM3NxjlwgTLKvro6AEzJyTjLLyLt05/GUVFOfFHRxAlwPq8RxJqroflAdHBzt4fHWR2QUQQzK2Dpp4zrGcWQNhusgYPJh9pDd9X6cX1LQgghhBg7I1mhKwcOaq1rAZRS/wNcD+ztN+7/At8CvjKC1xJiSjjcfpgH/v4AhzsO8+DyB/n0wk/z2uHXzqnLpTg7b2srPZu3hI4S6DtotNg3JSTgKCsLuhAEAAAgAElEQVQj5ZOfxFlRTvy8eSiTKbaTdXdEh7XgqtvpWvB7wuMScoywtugWI7AFg1vSdDjbewh2szyXLpdCCCGEmJRGEuimA8civq4HKiIHKKWWAjO11n9USg0Z6JRSnwM+B5CXlzeCKQkxcf3t6N/42jtfw2Ky8KOrf8SK3BUArClcIwFumHwdHfRUVoaOEujdvx8A5XDgWLaM5Ouvx1lRgW3BApQlBluGtYaO4wNLJJtroDNi/6TJAmmFRlCb9zHImBsIb3PANsLS29JbJcAJIYQQU9hIfsIZrD4p1DJTKWUCngQ+fbYn0lo/BzwHRpfLEcxJiAnH5/fxzM5neK7qORakL+DJlU8yLWGSdEmcYHxd3bi2bQ3tgXPv2wd+Pyo+HvvSpWTevw5HeQX2kkUoq3X8JuZxGytrUcHtADQfBE93eFx8MmQWw+wrwyttGcXGEQLmcZyvEEIIIaaMkQS6emBmxNczgBMRXycCi4A3A3tTcoBXlVLXSWMUcaFo723nobcf4t3j73LDnBv4+oqvE2+Oj/W0Jg2/y0XPtm2hPXCu3bvB50NZrdgXLybjC1/AUVGOffFiTPHj8Ln2nA6vsjVF7G9rqwPtD49LzjMC27JLooNbQhZMlL16QgghhJgSRhLotgBFSqlZwHHgk8DtwTu11u1A6CRapdSbwFckzIkLxf7T+7n/7/dzqucUj6x4hI8Xf3ziNN6YoPy9vbh27AztgXPtrAKPBywW7IsWkf6Zz+CsKMe+dCkmu32MJuEzAlr/EsnmauhpCY8zxxthbdoSo6QxuL8tfQ7EOcdmbkIIIYQQ/Qw70GmtvUqpLwF/wji24Gda6z1KqceASq31q6M1SSEmm42HNvLYPx4jKS6Jn6/+OYszF8d6ShOS7uvDtXt3aA+ca/t2dG8vmEzYFiwg7c5P4VyxAseyZZicoxyS+roDQa1fcGs5CL7e8DhHRmBv21rInBsObskzwWQe3TkJIYQQQpwnpfXE2rJWVlamKytlEU9MTh6/h+9Wfpdf7fsVy7OX853Lv0OGPePsD7xAaK8X9969oT1wPdu2oV1GS/34efNwVlTgqKjAUbYcc1LSKLyghq5T0YEtWCrZEXHotjIZ+9gy5kaXSGYUycHbQgghhBh3SqmtWuuycxkbg7ZvQkxNza5m/vXNf2Vb4zbumH8HD5Y9iNV0YTe60D4f7v37Q3vgeior8XcbTULii+aQctNNOCrKcVx0EZbU1OG/kM8Dpw8HGpFENiapgd6O8Li4BCOkFVwaHdzSCsEiexuFEEIIMflIoBNiFOxo3MGDbz5IZ18nT3zoiQv2GALt99NbczC0B65nSyX+duMw7LiCApLWrsVZUY6jvBxLxjBWLl1tg+9taz0Mfm94XOI0I7CVfiK80pY5FxJzpSmJEEIIIfj99uN8+08HONHmYlqKna+umssNS6fHelrDIoFOiBHQWvPSgZd4YssT5DhyePZjzzI3bW6spzVutNb0HT5sBLj3P6Bn82Z8ra0AWGfMIPEjVxl74MrLsWZnn9uT+v1GOWT/lbbmaqN8MshkhfTZkDUPFlwfceh2EcQnjsG7FUIIIcRU8Pvtx3n4lV24PD4Ajre5ePiVXQCTMtRJoBNimNxeN//x/n/wh0N/4LLpl/HEh54gOX6Eh0BPcFprPEeP0v3BB0YZ5ebNeJuaALDk5JDw4Q/jqKjAWVGOdfpZviF6XNByKCKwBcslD4LXFR5nSzFW14qujtjbVgwp+WCWb2FCCCGEGFqn20NdSw9HT/cELrt5Zdtxer3+qHEuj49v/+mABDohLhTHu47zwN8fYN/pfXx+8ef5wuIvYFKmWE9rTHiOH6c7sAeue/NmvA0NAJgzM3CWV+CoKMdZUYE1L2/gsQxaG63++5/b1lwNbUeBYFMmBSl5RlAr+HD0/jZnhpRJCiGEEGJQWmuauno52mIEtrqWbupC4a2H0919UePTnXEDwlzQiTbXoLdPdBLohDhP7514j4feegiv38v3r/w+K2eujPWURpXnVCM9mz8wVuHe/wBPvdEN0pyaiqO8HMdnP4NzxQriZs0KBzifF07XRuxtiyiTdLWGn9xih4w5MKMMltweCG5zjdJJ6xidKyeEEEKISc3r83O8zWUEttM9HG3pDgW2o6d76OnzhcaaFExLsZOf7mDVwhzy0x3kpznIS3eQn+4kId7CpU/8jeODhLdpKZPzZxEJdEKcI601P9v9M763/XsUJhfy1BVPkZ+UH+tpjZi3pYWezZtDRwn0HTkCgCkpCcdFF5F25504KiqIL5qD8gTPbtsCf/tVOLy1HAK/J/ykziyjTHLhjRF724ohaQaYpuZKphBCCCGGr6fPGy6LbOmh7nQ4tNW3uvD5w0etxVtM5KU5yE93cMnsDPLTA4EtzcGMVAdxljP/rPHVVXOj9tAB2K1mvrpqcvZBkEAnxDno9nTzyLuP8Je6v7CqYBWPXfIYDqsj1tMaFm9rKz1btgT2wH1Ab81BAExOJ46yMlJu/TiORYXYUv2o1kPQtBPe/y38sQY6T4SfSJmNdv8ZxVC8OmJ/2xywj+AIAiGEEEJMOVprWns81LV0h4LbkZbuQHjroamzN2p8st1KfrqDkunJrC3NJT/dSX6ascqWlRiPyTT87RjBfXJTpculHCwuxFnUttdy/9/vp66jjgeXP8idC+4cuFdsAvN1dtKzpTK0B653/37QGmW34Vg0D8fcHJwzLNicbajWGmMFrq8r/ATxSRF72iL2tqXOAktc7N6YEEIIISYUn19zssNNXXPkPrbu0KpbZ683anxOks0oiQyUQwZX3fLTnCQ7LuyzfOVgcSFGyRt1b/C1d79GnCmO565+jorcilhP6az83d30bNsWOkrAvXcv+P0oqxl7QRoZl2fjTD2N3VqHMtWCFziCUQ6ZUQRL/gkyI7pJJmRLUxIhhBBCAOD2+KhvDTYgCXaPNAJc/WkXfb5wwxGrWTEz1SiHLMtPJS+0yuZgZpoDm9Ucw3cydUigE2IQPr+PH+z4AT/e9WMWpS/iySueJMeZE+tpDcrvcuHaupXut/9Kzwcf4Ko5Cj4/mMCe6SdjfjeOrF7sGX2Y4k5B+hzIWAIZt4ZX3dLnQHxCrN+KEGKSmUoH8wohwtpdnuh9bBHXT3a4iSzwS4i3kJfmYG52IlcvyCY/zRladctNtmMeQWmkODcS6ITop83dxkNvP8R7J97jpqKb+PeKfyfeHD92L1j1ErzxGLTXQ/IMuGo9lN46+Ni+HvwN+3D9429GGeWew7iO/f/s3Xd4lFXexvHvSc+kd0ilF5UiRsCOFSwgu9YV9LWwqCsq4Krr6lrX3gF7d0FZC3ZXsYCdJiiKShFJSIH0kF5mzvvHDCGhBjIh7f5cF1dmnnnmOWdgSHLPKb8yrBMwluDoOmL61eBIDcQxsDc+if0brW3r667d5qNPw0Sk5TpbYV6RrsRaS15ZTcM2/5lFlWwo9OweWVRJSWVdk/NjQwNJi3FwWK8YUmMc9IgJadiEJDokoEMtRemMFOhEGvm18FemLZxGXmUetxx2C2f2O7N1G1z5Grx3lbvINkDpRvf96lKIGwAFa7CbV1P100oqf9lIRWYVVQUBWKcBLEFxPkQNjyVkUD+Ch4/AN2WQp3ZbTOv2W0S6vPs//q3JDnHgLsx7z0e/cerg7vj7akdbkbZU53SRXVzl3nikqPEUSff96rptUyN9fQyJkUH0iAnh1EHd3btGekbaUqMdhAQqMrRn2hRFxOO939/jtu9uIyIwgodHPczguMGt3+jDB1H6YwF5K8Oor/TFz+EkblAZgeH1VOYFUrE5gMqCQGy9+5OvwOQoHEMGEnLE0TiOOQXfmLjW76OIdHmF5TWs2VzO2rwy1mwuY83mcpb8UbTb54QF+hEZ4k9kcACRDn8iHQFEeb5GBvsTFdLotsN9TniQf4t2rhPpaipq6ptsPOKu0eaeHplTUt1kq/8gfx/SoreNrLm3+nevaUuKCtaHMO2MNkUR2Qt1zjruX3Y/r/72KukJ6dx/zP3EBsful7ZLfywgd2kE1un+Jlpf6Ufu4kjA/QtNQM80Is8+HMeIkTiGH4pflMoBiEjrKa6odQe2vHLWbnaHt7WbyymsqG04JyzQj74JoTgCfJsU890qItifi4/oSXFlLaVVdRRX1lJcWcfGokqKK+vYUl3Hrj5L9jHu50d6Al6UJ/Btu7/dY577IQG+mvIlnZK1lsKK2h12i8zwbERSUF7b5Pwohz+pMSEcnBLF+KEOz66RIfSIcRAXFqj/J52UAp10afmV+VzzxTWsyFvBBQdcwLRDpuHns5/+W/zyLpt/CG8Ic9sYfIOg1ydf4henETgR8b7SyjrW5G0LbFtH3QrKt9WBCg30o098KMcPjKdfQhh9E8LolxBKt/AgjDE7rKEDd2He28YduNs1dE6XZYsn6JVU1VFSWUtxRV3D7ZJKz2OVdeSVVbN6UxkllbVU7CQ8buXva4gIdo8ARjkCiPCEv223twXDqEajhtphT9oDp8uSU1LlWcfmqcvWMNpW0eS9bwx0Dw8iNcbB8QMSSIt1NGxCkhrjIDyoa2/131Up0EmXtSJvBdMXTqeiroL7jr6Pk3uevH8arqum+sWryZ/7Mc7q4J2e4qxBYU5EWqy0qs4z0uYObevy3F/zGhXwDQnwpU9CGMf2j6NfQhh9EkLplxBGYkTQbj/N39fCvL4+hqiQAKJC9q6OZW29i5KqWkor6yj2hL7Sym0jgKVVW4NhLRuLKlmZ5T5eW+/a5TWD/X0bRvl2NQ1021TRbY/5aWqa7KXqOmejdWzb1rRlFlWSVVxJnXPbsHWArw8p0cGkxYQwomd0w46RqdEhJEcF64MI2YHW0EmXY61l7uq53LfkPrqHdueRYx+hX1S//dJ2zdLPyb99GmVra/EJ8ge/QFzl5Tuc55eYSN/PP9svfRKRjq+sus69xs0T3rauddu8ZVtwC/b3pW9CKH3j3SNt7lG3UBIjgjv1urWqWmfDiF+JZ1Sw8f3iyrpGt7dOE61rsvZoe1vXB0Y5AojYTfiLahQOw4L8OvXfs0BJZW2TkbXGa9o2balucm5YkF9DAe2ta9pSPcW1u4UHaat/0Ro6kV2prq/mjkV38O7v73J08tHcfdTdhAeEt3q7Nev/oODO69nyzUp8/CH27JOIvuYOyr/8ktx/3Yyt3vaN3gQFET9taqv3SUQ6nvKaetY2nibpWeuWW7rte0iQvw994kM5ondswzTJfglhJEV27uC2K8EBvgQHBJMYufMZETtjraWspr5hBLDxNNBtt7eGw71bH9hkGqjDPf1zhxDouR/lCMCh9YHthstl2VxW3aQu24attwsr2FJd3+T8+DD3Vv9H9IltNMrm3vI/0uGvf1fxGgU66TKyyrKYtnAavxX9xt+G/I1Lh1yKj2ndaTO1GRkUzJpJ6fsfYHxcxIyIIPr2F/BLOxCAiLFjAch7+BHqc3Px696d+GlTG46LSNdUUVPfMD1y7davm8vJLqlqOCfAz4c+caGM6BntCW7u8JYc5dCn+y1kjCE8yL3rZkq0o9nPa7w+sOk00G0jgFtDYXPXBwb4+hDh8N9uGujW29s2i4kIdq8P3DpqqGl5+6a23kVWsWfTkYKKRrtGVrKxqJKaRlN4/XwMSVHBpEY7GJKS6K7N5tmEJDXaQXCA/g1k/9CUS+kSvs3+luu+ug6Xy8XdR93NMSnHtGp7tVnZFDz5BKVvvYXBRVSfcmIunIjfuNvBVwuWRcStqtbZENzW5G0becsqbhTcfH3oFRfSENi2hrfUaAW3zmLr+sDGI4A7Wx+4/c6he1ofGOXwJ2KHaaDbNoVpPFU00hMau8L6wLLquob1a022/C+sJLe0isazbYP9fRtG19IaApt7qmRiZFCX+PuStqEplyIe1lqe+/k5ZiyfQe/I3jx67KOkhqe2Wnt1ubkUPPUUJW++icES1aeSmGG++E94Efoc32rtikj7Vl3nDm7utW3b1rptLK5smKLn72voFRvK0JRIzk5PaQhvadEO/dLYyQX4+RAfFkR8WNBePW/79YHFlXWNguHW9YHu+6s3lTVvfWCQX9MRwGD/JsGw8ZTRreGwtdYHvr0ie6833QH3z/788poddovcOtrWuAwHQExIAKkxDg7tEUVqTHKjGm0O4kK11b+0fwp00mmV15Zz0zc38VnmZ5zc42RuPfxWHP7NnzqzN+o251H49NOUvPYa1lqiDokjJmEF/gceCX9+BsK6tUq7ItK+VNc5WZ9f0aQA99rNZWQWVTZ86u/nY+gZG8KgpAj+PCypYeQtLSZEhX1lr3hjfWDj0Lez9YEZhRWU7MX6wMYjfg3lIkK23zl0z+sDty+LkV1SxQ3zfgLcO6zWO13klFSTsbU2W1ElGwrcu0dmFlU2qZHoY6B7RDBpMQ5OOjCBVM82/1vXtIVpq3/p4DTlUjql9SXruXrB1Wws28j0Q6Zz/gHnt8onbPUFBRQ+8yzFc+dinU4iRx9NbPQ3+NdnwKgb4KhrwEdz6EU6m5p6J38UVDQabXNPl9xQWNEQ3Hw9wa1fw86S7uDWI1bBTTqevVkf2HjUsDnrA3c2DfTVJZk7bDICEOjnQ7eIILKLq6hvNNIY4Ofjng7p2S2yR8y23SOToxwE+On/nHQsmnIpXdqnGZ9y49c3EuQXxDMnPcOh3Q71ehv1xcUUPfccRXNewdbUEHH6OGKPiCZgxX0QFANnvAc9jvR6uyKyf9XWuzzBrWxbPbe8MjIKKxumrfn6GNJiHPRLCOO0wd0b1rj1jA3RL5HSabS0fuAOI4CeeoKN1wdmFlXyY1bJbtcH1tS7GJQUwWmDu2/b8j/GQUJYUJfcxVUEFOikE3G6nMxcMZPnfn6OQbGDeGjUQ3QL8e5UR2dJCYUvvkjxy//BVVVF+GmnEXvJRAJ/vA+WPQ59ToQ/PQkhsV5tV0RaV53TxQbPiJt7Z0l3eNtQUNEwCuBjIC0mhL7xoZxyUHf6esoB9IoLIdBPI/EiO7Mv6wOttRxxz+fklFbv8FhSZDCzzhvmzS6KdHgKdNIpFFcXc92X17EodxFn9juTG4bfQIDv3n2KuDvOsjKKXnyJopdewlVeTtjJY4ibMoXAwGJ4YwJsyYET74DDpoCPPpEXaa/qnS42FFY2GW1bu7mMPwoqqHO6g5sxkBrtoG98GCcdkNBQgLt3XKi2ghfZD4wxXDdmQJM1dODecfLa0f3bsGci7ZMCnXR4vxT+wrQF08ivyufWw27ljH5neO3azvIKimf/h8LnX8C1ZQthJ55I7JQpBPXtA9/Ngs9ug7BEuOgjSPH+1E4R2TdOlyWjsNEaN08B7vX5FdQ6t03lSokOpl98GMcNSGgowN07LlT1o0Ta2NbdLPdll0uRrkaBTjq0d9a9wx2L7iAyMJKXxrzEoLhBXrmuq7KS4ldeofDZ53CWlBB67LHEXTmFoAMOgIpCePUcWDsfBpwGp8+C4CivtCsie8fpsmwsqmxSgHvN5nJ+zy9vsgYnKTKYfgmhHNMvzrPGLZQ+8aE4AvRjUKS9Gn9wkgKcSDPoJ5l0SHXOOu5dei//Xf1fhncbzn1H30dMcEyLr+uqrqZ47lwKn3kWZ2EhIUcdRdxVVxI8yBMUN3wDb06CygI45QE4dJJ7fpaItCqXy5JVXLVDAe51eeXUbBfc+iaEclTfWPrEu0fc+saHEhKoH3ciItI56SecdDh5lXlMXzidH/N/5MIDL+TqYVfj59Oyt7KrpoaS116n8Omnqc/PJ+Tww4idciWOYQd7TnDCVw/BwrsgqgdM+hS6D2n5ixGRJlwuS3ZJVcOmJFvLAazLK2+ylqZ7RBB9E8I4rFdMwxq3PvGhqiclIiJdjgKddCjfb/6eaxZeQ2V9Jfcfcz9jeoxp0fVsbS0l8+ZR8ORT1G/ahCM9ncQHHyBk+PBtJ5Vthnl/hT++gIPOhLGPQGBYC1+JSNdmrSe4bS5vCG9rPdMmGxcETggPpF9CGH8Znuqu5+YJb+EKbiIiIoACnXQQ1lpe+e0VHlj6AElhSTxz0jP0jeq779erq6P0nXcoePwJ6nJyCB46lMS778IxcmTTAuS/fw7zJkNNOYybCQefrymWInvBWktuaXXDSNsazwYl6zaXNSk4HBcWSL+EUM5OT2kowN03PowIh4KbiIjI7ijQSbtXVV/F7d/dzvvr32dU8ijuPOpOwgPC9+latr6e0vffdwe5zEyCBg2i2223EXLkEU2DnLPePb3yq4cgrj/833sQP9BLr0ik87HWsnlLjWdTEk94yytj3eZyymrqG86LDQ2gb3wYZx6S3FCAu19CKJEO75UZERER6UpaFOiMMWOARwFf4Flr7T3bPX4ZcAXgBMqBydbaX1rSpnQtG8s2Mm3BNNYUr+GKoVcwefBkfMze13mzTidbPvwfBY89Ru2GDQQeMJDkJx4ndNSopkEOoDTLvfFJ5nfuEbmT74MAh5dekUjHZq0lv6xmhwLcazeXsaV6W3CLDgmgb3wo4w9Oapgq2S8hjOgQBTcRERFv2udAZ4zxBR4DTgSygKXGmHe3C2yvWGuf9Jw/DngIaNmiJ+kyvs7+muu/vB6LZdbxszg6+ei9voZ1uSibP5/8WbOoXfc7gf36kTRzBmEnnLBjkANY/T94+3Jw1sGfn4HBZ3vhlYh0PNZaCsprPQW4t9VxW7O5nNKquobzIh3+9IsPY+yQxIbNSfolhBEbGtiGvRcREek6WjJCNxxYZ61dD2CMmQucDjQEOmvtlkbnhwC2Be1JF+GyLp796VlmrZhFn6g+PDrqUVLCU/bqGtZayj/7jPyZs6hZvZqA3r1JevghwkaPxvjsZISvvhY+vRUWPQbdBsGZL0JsH6+8HpG29PaK7D0W5i0sd4+4uUfbto24FVduC27hQX70SwjjlEHdGwpw900IJS40cOcfjoiIiMh+0ZJAlwRsbHQ/Cxix/UnGmCuA6UAAcNzOLmSMmQxMBkhNTW1Bl6SjK6st48avb2TBxgWc0vMUbjnsFhz+zZ/uaK2l/IsvKJgxk+pffiEgLY3E++8n/JSTMb6+O39S0R/wxsWQsxyGT4YT7wD/IC+9IpG28/aKbG6Y91PDdv/ZJVVc9+ZKvl5XgCPAt2GtW2FFbcNzwjzBbcxB3egbH9YQ3OLDFNxERETao5YEup39ZN9hBM5a+xjwmDHmPOAm4P92cs7TwNMA6enpGsXron4v+Z2pC6aysWwj1x96PRMGTmj2L5DWWiq+/ob8mTOpXrkS/5QUut99NxFjT8P47eZtvuotePcqwMDZ/4EDxnnnxYi0AZfLkldWw8biSjYWVXLLu6ua1G4DqK138cb3WYQG+tE3IZQTBiY0TJPslxBGQriCm4iISEfSkkCXBTSeB5cM5Ozm/LnAEy1oTzqx+Rvmc9M3NxHsF8wzJz3Dod0ObfZzKxYtIn/GTKqWL8cvsTvd7ridyPHjMf672e68rho+/icsew6SDoEzn3cXDBdpx6y1FFfWkVVcycaiqobgtrG4iqyiSrJKqqitd+3xOgb46daTFNxEREQ6gZYEuqVAX2NMTyAbOBc4r/EJxpi+1tq1nrunAmsRaaTeVc+MFTN44ecXGBw3mIeOeYiEkIRmPbdy2TLyZ8ykcskS/BIS6HbrLUT++c+YgD3solewFl6/EDb/DIdfCcfdDH7aeU/ah4qaek9Qq/KENfftrOJKsoqrKG9UAgDcm5KkRDkY0D2MEw9IIDnaQUpUMCnRDs5/djE5pdU7tJEYGawwJyIi0knsc6Cz1tYbY6YAH+MuW/C8tXaVMeZ2YJm19l1gijHmBKAOKGYn0y2l6yquLubaL69lce5izu53NtcPv54A3z0Hq8oVKyiYOZOKb7/DNy6WhBtvJPLss/AJbMauej/Ohfeng18gnPc69DvJC69EpPlq611klzQNaxuLK8nyjLQVNVrPBhDs70tKdDApUQ5G9ooh2RPWUqIcpEQHExa065Ho68YMaLKGbuv1rh3dv9Ven4iIiOxfxtr2tWQtPT3dLlu2rK27Ia1sVcEqpi2cRmFVITeNvIk/9f3THp9T9dNP5M+cScWXX+EbHU3MX/9K1Lnn4BMcvOcGayvgw2vhhzmQejic8SxEJO35eSJ7yemybNpS3RDQtga3LE9w27Slmsbfdv19DUmR7pCW7Alp7rDmHmmLDglo0Whac3a5FBERkfbFGPO9tTa9Oee2qLC4yL54a+1b/HvRv4kOjublk1/mwNgDd3t+9a+/kj9zFuWff45vRARx10wnesIEfBzN3P1y8yr3FMuCtXD0dXDM9eCrt77sG2sthRW1DWvXNhZVNlnTllNSRZ1zW2IzBrqFB5ES5eCw3jFNwlpKtIOE8CB8fVpv+uP4g5MU4ERERDox/VYr+02ts5Z7l9zLa2teY0T3Edx39H1EB0Xv8vzqNWsomPUYZfPn4xMeTtzVVxF1/vn4hoY2r0FrYflL8L/rITAcLngbeo3yymuRzq2suq7JpiNZjUfaiquorG26c2RMSADJUcEclBTByQd1bzLKlhgZRKDfLkpmiIiIiLSQAp3sF5srNjP9i+mszF/JRQddxFUHX4Wfz87ffjXr11MwaxZb/vcRPiEhxF5xBdH/dwG+4eHNb7B6C7w/FX5+E3odC39+GkLjvfRqpKOrrnO6Q1qjtWsNwa24kpJGBbUBQgP9SI4KJi0mhCP7xDUJbMlRwYQE6lupiIiItA39FiKtbtmmZVzzxTVU1VfxwDEPMLrH6J2eV5uRQf5jj7Hl/Q8wQUHETJ5MzEUX4hsZuXcN5qyA1y+Ckkw4/mY4Yhr4+HjhlUhHUe90kVta3WTtWuMpknllNU3OD/D1ITkqmORoB4OTI5psOpIS5SDS4a9dIUVERKRdUqCTVmOtZc6vc3hg2QOkhKXw/Ojn6R3Ze5nijnIAACAASURBVIfzarOyKHj8CUrfeQfj70/0RRcSc8kl+EXvejrmLhqExU/B/Jvco3EXfgBph3np1Uh7Yq0lv6zGXX9ta1jbGtyKK8kpqcbp2raOzcdA94hgUqKDOaZfXMPI2tbgFh8WiE8rrmMTERERaS0KdNIqquqruPXbW/nwjw8ZlTKKu468i7CAsCbn1OXkUPDkU5TMm4fx8SF64gRiJk3CLy5u7xusLIJ3psDqD6DfGBj/BDj2MhBKu1JaWddoZK1yhzVtNdsV0I4NDSQlOpiDU6IYN6TxTpEOukcG4e+rUVoRERHpfBToxOs2btnI1IVTWVu8lisPvpJJgybhY7b9Ml23eTOFTz1F8etvYICoc84hZvJk/BP2cY3bxiXwxsVQtglG3w0jL3dvLSjtWlWt0z26tpMi2huLKymrblpAOyzIj5QoB33iQzm2f7w7rHmmRCZHOQgO0MYjIiIi0vUo0IlXfZX1Fdd/dT0Gw+MnPM6RSUc2PFafn0/BM89QMve/WJeLyDPOIPbSyfgnJu5bYy4XfPsofHYHRCTDJR9D0iFeeiXSUnVOFzklVTvuFukJbQXlTdexBfr5NGznn94jqmENW3KUe5QtwrHrAtoiIiIiXZUCnXiFy7p4euXTPP7D4/SL6sfDxz5MSlgKAPVFRRQ++xzFr7yCrasjYvzpxF5+OQHJyfveYHk+vHUp/P4ZHHA6jJsJQRFeejXSHC6XJa+sZtu0yO2CW25pFY2WseHrY0iMdNdjO35AvHt0rVEx7bjQQG08IiIiIrKXFOikxcpqy/jnV/9kYdZCTu11KrccdgvBfsHUFxdT9MKLFM2eja2uJmLsWGL/djkBaWkta/CPr+DNSVBVDKc+BOkXa4plK7DWUlxZ16Ro9tadIrOKKskqqaJ2u3VsCeGBJEc5OLRHFCnRSe7pkJ5pkd0jgvDTOjYRERERr1KgkxZZW7yWaQunkV2WzT+G/4PzBpyHq6yM/Befpeill3BVVhJ+8snETrmCwF69WtaYywlf3g9f3AvRvWHiG9BtkHdeSBdVUVO/0zVsWZ4C2uU1TdexRTr8SYlyMKB7GCcekECyZ4pkSrSDpMhggvy1jk1ERERkf1Kgk3320YaPuPmbm3H4OXh29LMMDelPwRNPUPTCi7jKyggbPZrYK/5GUL9+LW9sSy7M+yts+AoGnwunPgiBoS2/bidXW+8iu2THDUe2FtMuqqhtcn6wv2/DRiMje8U02do/JTqYsCCtYxMRERFpTxToZK/Vu+p5dPmjvLjqRYbEDeHB4f/Gd94n/P7cFThLSwk9/njirpxC0IAB3mlw7afw1mSoq3KXIxh6nneu2wk4XZZNW6obAtrW4La1mPamLdXYRuvY/H0NSZHukDY6Mdyzfm3bKFtMSIDWsYmIiIh0IAp0sleKqou49otrWbJpCef1OINL1iVTMm4CzqIiQo45mrgrryL4oAO905izDj7/N3zzCMQfAGe9CHH9vXPtduTtFdnc//FqckqqSIwM5trR/Rl/cBLgXsdWWFHbsHbNveHItpG2nJIq6pzbEpsx0C3cvfHIYb1jGtVicwe2hPAgfFVAW0RERKTTMLbxx/ftQHp6ul22bFlbd0N24ueCn5m2cBplZYXcU3Q8iW8twllQQMgRRxB35RSChw71XmMlmfDGJZC1BA65CMbcDf7B3rt+O/H2imxumPcTVXXOhmO+Pob+CaHUuyxZxVVU1jqbPCc6JICUqGDP+rVttdhSoh0kRgYR6Kd1bCIiIiIdmTHme2ttenPO1QidNMu8tfO45+s7OO2XYM78LhhT8D6BI0YQ98jDONKb9V5rvl/fh3f+5q4zd+bzcNAZ3r1+O1Fd5+TW91Y1CXPgnka5ZnM5xw6I58g+cdtqsXmCW0ig/tuKiIiIiJt+M5TdqnXWcs+3d1I07w1mLvInvLiY4GHDiHvgIUJGjvBuY/U18MnNsPhJ6D7UHeZienu3jXZgfX45cxZn8sb3WZRW1e30HKfL8swFXg7KIiIiItLpKNDJLuWWZvPSQxdz9MeZJJRA0OD+xD1wFSGHH+79jTMKf4c3LoLcH2HE5XDibeAX6N022lCd08Unv2xm9qIMvv29ED8fw+iDurF4fSEF5bU7nJ8Y2fmml4qIiIiI9ynQyQ6s08kPc2ay5clnOb3ISW3fFFLuvZGQo49unR0Qf3oD3psKPr5w7isw4FTvt9FGckqqeHVJJnOXbiS/rIYkz6YnZ6UnEx8WtNM1dMH+vlw7uvNt/iIiIiIi3qdAJw2sy8WW/33E7w/dRXB2IUXdAvC77wYGjD2vdYJcbSV89A9Y/hIkD3dPsYxM8X47+5nTZflybT5zFmXw+W95WODY/vFMGJHKqP7xTXaZ3Lqb5a52uRQRERER2R0FOsG6XJR9+il5M2ZQt+53CmLh10mDuXjKM4QFhbdOo3m/uadY5v0CR06DY28E345dtLqgvIbXlm3klcWZZBVXERsawOWjenPuoamkRDt2+bzxBycpwImIiIjIPlGg68KstZQvWEj+zJnU/Por+XEBvHK6L+l/uYorh0zCx/i0RqPwwyvw4d/B3wET3oS+J3i/nf3EWsuSP4qYvTiTj37Opc5pGdkrmn+cPICTDuhGgF8r/B2KiIiIiHgo0HVB1loqvv6a/Bkzqf7pJ5yJcbww3sHSQUHcPeo+jkg6onUarimHD66BlXOhx1Hw52cgvHvrtNXKtlTXMe/7LOYszmRtXjlhQX5MHJnGhBGp9IkPa+vuiYiIiEgXoUDXhVhrqfzuO/JnzKTqhx/wT0rit0uP57aIhfSNG8grox4mOSy5dRrPXemeYlm0Hkb9E47+u3sTlA7mp6xSZi/K4N0fc6iqczIkOYL7zhzM2MGJBAd0vNcjIiIiIh2bAl0XUbFkCQUzZlK5bBl+3bsTefM/uDtmMQs2fcHYXuP412H/ItivFbbKtxaWPQcf/ROCo+CCd6HnUd5vpxVV1Tp578ccZi/OYGVWKcH+vpw+NJEJI9IYlBzR1t0TERERkS5Mga6Tq1y+gvyZM6j8bhF+cXEk/Osm8k8YyiXfXEvu5lz+OeKfnNv/3NbZxbKqBN67Cn55B/qcAOOfhNA477fTStbllTF7USZvLs+irLqevvGh3DbuQMYfnEREcMfewEVEREREOgcFuk6qauVK8mfMpOLrr/GNiSHhhn8Qec45fJy7gFs+vYgQ/xCeH/M8B8cf3DodyP4eXr8ISrPghNvg8KvAp/1vEFJb7+LjVZuYvSiDxX8U4e9rOPmg7kwcmcahPaJaJ/iKiIiIiOwjBbpOpmrVKgpmzqJ84UJ8IyOJv/bvRP3lL7iCAnjw+4d5+ZeXOTj+YB485kHiHK0wWmYtfPcYfHorhHWDiz+ClOHeb8fLNhZV8uqSTF5btpGC8lpSooO5fswAzkpPJjY0sK27JyIiIiKyUwp0nUT16tUUzJpF2Sef4hMRQdzUqURNnIhvaAiFVYVc+8kUlm5ayl8G/IVr06/FvzVqvlUWwduXw5qPYMBpMG4mOKK9346XOF2WhavzmL0og4Vr8jHAcQMSmDgylaP7xuHjo9E4EREREWnfFOg6uJp168if9RhlH32ET2gosVdOIfqCC/ANc2+dvzJ/JdMXTqekpoQ7j7yTcb3HtU5HMr6DNy+Binw4+T4YPhna6fTEvLJqXlu6kVeXbCS7pIr4sECuPLYP5w5PJTGyFTaGERERERFpJQp0HVTNH39Q8NjjbPngA3yCg4m5/DJiLrwQ34htuy6+seYN7lp8F/GOeP5z8n8YGDPQ+x1xueDrh2DBXRCZCpfMh8RWWpfXAtZavltfyJxFmXy8ahP1LssRfWK46dSBnHBAAv6+7X99n4iIiIjI9hToOpjazEwKHn+C0nffxQQGEjPpEqIvvhi/qKiGc2qcNdy9+G7eXPsmhycezr1H3UtkUKT3O1O2Gd6aDOsXwkFnwGmPQFC499tpgdLKOt5YnsWcxRmsz68gItifCw/vwXkjUukVF9rW3RMRERERaREFug6iLjubgiefpGTeWxg/P6IvuICYv07CLyamyXmbKjYxbcE0fi78mb8O+itXDL0C39Yo4P37Apg3GWq2wNgZMOyCdjPF0lrLDxtLmLM4k/d+zKGm3sXBqZE8eNYQTh3cnSB/FQAXERERkc6hRYHOGDMGeBTwBZ611t6z3ePTgUlAPZAPXGytzWhJm11N3aZN7iD35jwMEHXeecT8dRL+8fE7nLskdwnXfnktNc4aHhn1CMenHe/9Djnr4Yt74MsHILYfXPA2JBzo/Xb2QUVNPe/+mMPsRRmsytmCI8CXMw5JZsKIVA5MVAFwEREREel89jnQGWN8gceAE4EsYKkx5l1r7S+NTlsBpFtrK40xlwP3Aee0pMNdRV1eHoVPP0PJf/+LBSLPPIPYSy/Fv1u3Hc611vLyLy/z0PcPkRaexiPHPkKviF7e71RpNrw5CTK/haET4ZT7ICDE++3spdWbypizOIO3lmdTVlPPgG5h3DH+IMYPTSQsSAXARURERKTzaskI3XBgnbV2PYAxZi5wOtAQ6Ky1CxqdvwiY2IL2uoT6wkIKn3mW4ldfxdbXE/nnPxFz6WUEJCft9PzKukpu/vZmPt7wMSeknsC/j/w3If6tELLWfAxvXQb1NfCnp2FI2+bymnon//tpE3MWZ7B0QzEBvj6cOrg7E0emMixVBcBFREREpGtoSaBLAjY2up8FjNjN+ZcA/9vZA8aYycBkgNTU1BZ0qeOqLy6m6PnnKZo9B1tTQ8TppxN7+WUE7ObvI2NLBlMXTGV96XqmDpvKxQdd7P0gU18Ln90G382ChEFw1gsQ29e7beyFjMIKXlmSyevLsiiqqCUtxsE/TxnAmYekEB0S0Gb9EhERERFpCy0JdDtLDnanJxozEUgHjtnZ49bap4GnAdLT03d6jc7KWVpK4YsvUvzSy7iqqgg/7TRi/3Y5gT177vZ5Czcu5IavbsDXx5cnTniCwxMP937nijfAGxdD9vdw6CQ46U7wD/J+O3tQ73Tx2W95zFmcyZdr8vH1MZwwMJ6JI9M4onesCoCLiIiISJfVkkCXBaQ0up8M5Gx/kjHmBOBG4BhrbU0L2utUnGVlFL30MkUvvoirvJywk8cQd8UVBPbps9vnuayLJ358gid/fJKB0QN5+NiHSQrd+XTMFvnlHXjnSsDCWS/BgeO938YebCqtZu7STOYu2cimLdV0Cw9i6gl9OffQVLpF7P9gKSIiIiLS3rQk0C0F+hpjegLZwLnAeY1PMMYcDDwFjLHW5rWgrU7DWV5B8ezZFL7wAq7SUsJOPIHYKVMI6t9/j88trSnlhq9u4KvsrxjXexz/Gvkvgvy8HGzqqmH+jbD0WUgcBmc+D9G7Hy30JpfL8s3vBcxZlMknv27G6bIc1TeW204/kOMHxOOnAuAiIiIiIg32OdBZa+uNMVOAj3GXLXjeWrvKGHM7sMxa+y5wPxAKvO5Z25VprR3nhX53OK7KSopffZXCZ5/DWVxM6KhRxF45heADm7fl/+qi1UxdMJVNlZu4acRNnN3/bO+vlytYB69fCJt/gsOmwPG3gN/+WZdWXFHLG9+7C4BvKKwkyuHPpCN7ct6IVNJi2n4nTRERERGR9qhFdeistR8CH2537OZGt09oyfU7A1d1NcVz51L4zLM4CwsJOeoo4q6cQvDgwc2+xgfrP+DWb28lLCCMF0a/wND4od7v6MrX4L2p7gD3l/9C/zHeb2M71lqWZxYzZ1Em7/+US229i/S0KKae0I8xB3VTAXARERERkT1oUaCTXXPV1lLy2usUPvUU9fn5OA4bSdyVM3AMG9bsa9S56nho2UPM/nU2w+KH8eCoB4kNjvVuR2sr4H/XwYrZkHoYnPEcRLTCmrxGymvqeXtFNrMXZfDbpjJCA/04Jz2FCSNTGdAtvFXbFhERERHpTBTovMzW1lIy7y0KnnyS+k2bCE4/hMQHHyBk+PC9uk5BVQF//+LvfL/5eyYMnMA16dfg7+PlItmbf4E3LoL81XDU32HUDeDbem+JX3K2MGdxBm+vyKai1skB3cO560+DOH1oIiGBeiuKiIiIiOwt/RbtJbaujtJ336Xg8Seoy84meOhQEu++C8fIkXu91u3H/B+ZvmA6W2q3cNeRdzG291gvd9bC8pfdI3OBYXD+POh9nHfb8Kiuc/LBylzmLM5geWYJgX4+nDY4kYkjUxmaEqkC4CIiIiIiLaBA10LW6aT0vffcQS4zk6BBg+h26y2EHHnkXocVay2vr3mdu5fcTYIjgf+c8h8GRA/wboert8D70+DnN6DnMfDnZyAswbttAH8UVPDK4gxe/z6Lkso6esWGcNOpAznzkGQiHSoALiIiIiLiDQp0+8g6nWz530cUPPYYtX/8QeDAgSQ//jihx47ap1GnGmcNdy66k7fWvcURiUdw79H3EhEY4d1O5/zgnmJZvAGOuwmOnA4+3tt4pM7p4tNfNjNncSZfryvAz8dw0oEJTByRxmG9YzQaJyIiIiLiZQp0e1D63nvkPfwI9bm5+HXvTtzUq/EJCKTgsVnUrF1HYN++JM14lLATTsD47FuNtNzyXKYtnMaqwlVMHjyZvw35G75eDFpYC0uehvk3gSMWLvwA0g732uVzSqqYuySTuUs3kldWQ2JEENec2I9zDk0hPlwFwEVEREREWosC3W6Uvvceuf+6GVtdDUB9Tg651/8DrCWgd2+SHn6IsNGj9znIASzKXcR1X1xHrauWR499lONSvbyWraoY3pkCv70PfUfD+CcgJKbFl3W5LF+uzWfO4kw++3UzFhjVL467RqRx7IB4fH00GiciIiIi0toU6HYj7+FHGsJcA2vxjYyk17vvYHz3fRTNWsuLq17kkeWP0DO8Jw8f+zA9I3q2sMfb2bgU3rgYynLgpDth5N+gBeEToLC8hteWZfHKkgw2FlURExLApcf05rzhqaREO7zUcRERERERaQ4Fut2oz83d6XFnaWmLwlxFXQU3f3Mz8zPmc2LaidxxxB2E+Ifs8/V24HLBdzPhs9shPBEung/Jh+zz5ay1LN1QzOxFGXz08yZqnS5G9Izm2tEDGH1gAoF+KgAuIiIiItIWFOh2w697d+pzcnZ6fF9tKN3A1AVT+WPLH0w/ZDoXHnihdzcLqSiAty6DdZ/AwHEwbiYER+7TpbZU1/HW8mzmLM5gzeZywoL8OG9EKhNGpNI3Icx7fRYRERERkX2iQLcb8dOmNllDB2CCgoifNnWfrvd55ufc+PWN+Pn48dSJTzGy+0hvddVtw9fw5iSoLIRTHoBDJ8E+hMWfs0uZvSiDd37IoarOyaCkCO49YxBjhyTiCNBbRkRERESkvdBv57sRMdZd0LvxLpfx06Y2HG8up8vJ4z8+ztMrn+aAmAN4eNTDJIYmeq+jLid8+QB8cQ9E94LzXoPug/fqElW1Tt5bmcOcRRn8mFVKkL8P44YkMnFkGoOT922ET0REREREWpcC3R5EjB271wGusdKaUq7/6nq+yf6G8X3Gc9PImwj0DfReB8s2uUflNnwFg86G0x6CwOZPh1yXV86cxRm8+X0WW6rr6RMfyi1jD+DPw5KJCPb3Xj9FRERERMTrFOha0eqi1Vy94Go2V27mXyP/xVn9zvLuerl1n8K8S6G2Ak5/DIZOaNYUy9p6F/N/2cTsRRksWl+Ev69h9IHdmDgyjRE9o1UAXERERESkg1CgayXvr3+f2769jfCAcF4c8yJD4oZ47+LOOlhwJ3z9MMQNdBcKjx+wx6dlFVfy6pJM/rs0i4LyGpIig7l2dH/OTk8hLsyLo4YiIiIiIrJfKNB5WZ2rjgeXPcicX+dwSMIhPHDMA8QGx3qvgZKN8OYlsHExDPs/GHMPBOy6/pvTZfliTR6zF2WyYHUeAMf1j2fiyDSO7henAuAiIiIiIh2YAp0XFVQVcM3Ca1iet5yJAycyPX06/j5eXIf224fw9uXgqocznoNBZ+7y1Lyyal5flsUrizPJLqkiNjSQK0b14dzhKSRHqQC4iIiIiEhnoEDnJT/k/cA1C69hS+0W7jnqHk7tdar3Ll5fA5/cAoufgG6D4awXIab3DqdZa1m0vojZizP4+OdN1Lssh/eO4Z+nDOSkAxPw9/XxXp9ERERERKTNKdC1kLWW11a/xj1L76GboxuzT5lN/+j+3mugaD28fhHk/gDDL4WT7gC/puvdSivreHN5FnMWZ/B7fgXhQX5ccFgPJoxMpXdcqPf6IiIiIiIi7YoCXQtU11fz70X/5p3f3+HIpCO556h7iAiM8F4DP8+Dd68CHx84ZzYM3FY+wVrLyix3AfD3VuZQXediaEok9585mLFDEgny9/VeP0REREREpF1SoNtHOeU5TF0wlV+LfuWyIZdx+ZDL8TFemtJYVwUf3QDfvwDJh8KZz0NkKgCVtfW8+0MOsxdn8HP2FoL9ffnTwUlMGJHGQUleDJMiIiIiItLuKdDtg+9yvuO6L6+j3lXPzONmMipllPcunr8GXr8Q8lbBEVfDcf8CX3/WbC5jzqIM5i3Ppqymnn4Jodx++oGMPziJ8CAVABcRERER6YoU6Pbgg/Uf8OjyR9lUsYluId0YEjuE+Znz6RXRi0eOfYS08DTvNfbDK/DBNeAfDBPeoKbncXz00ybmLMpkyYYiAnx9OGVQNyaMTCM9LUoFwEVEREREujgFut34YP0H3PrtrVQ7qwHIrcgltyKXQbGDePakZ3H4e2n7/5py+PDv8OOrkHYk2cfN4D+/1PH63M8prKglNdrBP04ewFmHJBMTqgLgIiIiIiLipkC3G48uf7QhzDVWUFXgvTC36Wd4/UJs4TrWHzCFO8pO44snf8MAJwxMYMLINI7qE4uPCoCLiIiIiMh2FOh2Y1PFpr06vleshWXPYz+6gUqfUK73v5X3l/clIbyCq47ry7nDU+geEdzydkREREREpNNSoNuNbiHdyK3I3enxlnBVllA493LiMj/kK9dgptVezgF9e/Pk6akcP1AFwEVEREREpHkU6Hbj6mFXN1lDBxDkG8TVw67ep+sVV9TyxcKPOXTZ30lw5fGoz3lUjJjCGyN60DM2xFvdFhERERGRLkKBbjdO7XUqQJNdLq8ednXD8eaw1rI8s4Q5izYQ8/PzXOszh1LfaL4+8iUuHXWqCoCLiIiIiMg+M9batu5DE+np6XbZsmVt3Y0WK6+p550fspm9KJPc3GweCnyG48wyytJOJOycp8ER3dZdFBERERGRdsgY8721Nr0552qEzst+zd3CnMUZvL0ih/Kaes6Iy+KNyAdx1BbBSfcQNuIyUP04ERERERHxAgU6L6iuc/LhT7nMWZzJ9xnFBPj5MHZQAtMdH5K4/CFMZAqcPx+ShrV1V0VEREREpBNRoGuBDQUVvLIkk9eXbaS4so6esSHcdOpAzuwfQOTHV8Kvn8OBf4Kxj0JQRFt3V0REREREOhkFuj14e0U293+8mpySKhIjg7nmxL44Av2YsziTr9YW4OtjOOmABCaMSOPw3jH4bPgSXv4rVJfCaY/AIRdqiqWIiIiIiLSKFgU6Y8wY4FHAF3jWWnvPdo8fDTwCDAbOtda+0ZL29re3V2Rzw7yfqKpzApBdUsX011cC0D0iiOkn9uOcQ1NICA8CZz0svAu+vB9i+8LEedDtoLbsvoiIiIiIdHL7HOiMMb7AY8CJQBaw1BjzrrX2l0anZQIXAn9vSSfbyv0fr24Ic41FhwTw1XXH4re1APiWHHhzEmR8A0POg1Puh8DQ/dxbERERERHpaloyQjccWGetXQ9gjJkLnA40BDpr7QbPY64WtNNmckqqdnq8uKJ2W5hbMx/evgzqqmH8kzD0L/uxhyIiIiIi0pX5tOC5ScDGRvezPMf2mjFmsjFmmTFmWX5+fgu65F2JkcG7Pu6sg/n/glfOgrDuMHmhwpyIiIiIiOxXLQl0O9vpY5+qlFtrn7bWpltr0+Pi4lrQJe+6dnR/gv19mxwL9vfllqNC4fkx8O0MSL8YJn0Kcf3aqJciIiIiItJVtWTKZRaQ0uh+MpDTsu60L+MPTiJp4/ukLL+feJtPnomjvOcY+nz1LlgLZ74AB/25rbspIiIiIiJdVEsC3VKgrzGmJ5ANnAuc55VetRcrX+PQn24BqsBAN/Lhj/9AZBpc8A5E92zrHoqIiIiISBe2z1MurbX1wBTgY+BX4DVr7SpjzO3GmHEAxphDjTFZwFnAU8aYVd7o9H7z2e1Qt5ONUaxTYU5ERERERNpci+rQWWs/BD7c7tjNjW4vxT0Vs2MqzdrF8ez92w8REREREZGdaMmmKJ1fxC6y6K6Oi4iIiIiI7EcKdLtz/M3gv13pAv9g93EREREREZE2pkC3O4PPhrEzICIFMO6vY2e4j4uIiIiIiLSxFq2h6xIGn60AJyIiIiIi7ZJG6ERERERERDooBToREREREZEOSoFORERERESkg1KgExERERER6aAU6ERERERERDooBToREREREZEOylhr27oPTRhj8oGMtu7HTsQCBW3dCenU9B6T1qT3l7Qmvb+kNen9Ja2pvb6/0qy1cc05sd0FuvbKGLPMWpve1v2QzkvvMWlNen9Ja9L7S1qT3l/SmjrD+0tTLkVERERERDooBToREREREZEOSoGu+Z5u6w5Ip6f3mLQmvb+kNen9Ja1J7y9pTR3+/aU1dCIiIiIiIh2URuhEREREREQ6KAU6ERERERGRDkqBrhmMMWOMMauNMeuMMf9o6/5I52KMed4Yk2eM+bmt+yKdizEmxRizwBjzqzFmlTHm6rbuk3QuxpggY8wSY8yPnvfYbW3dJ+l8jDG+xpgVxpj327ov0rkYYzYYY34yxvxgjFnW1v3ZV1pDtwfGGF9gDXAikAUsBf5irf2lTTsmnYYx5migHHjZWntQW/dHOg9j8FTykgAAIABJREFUTHegu7V2uTEmDPgeGK/vX+ItxhgDhFhry40x/sDXwNXW2kVt3DXpRIwx04F0INxae1pb90c6D2PMBiDdWtseC4s3m0bo9mw4sM5au95aWwvMBU5v4z5JJ2Kt/RIoaut+SOdjrc211i733C4DfgWS2rZX0plYt3LPXX/PH31SLF5jjEkGTgWebeu+iLRXCnR7lgRsbHQ/C/1CJCIdjDGmB3AwsLhteyKdjWc63A9AHvCJtVbvMfGmR4DrAFdbd0Q6JQvMN8Z8b4yZ3Nad2VcKdHtmdnJMnz6KSIdhjAkF3gSmWmu3tHV/pHOx1jqttUOBZGC4MUZTx8UrjDGnAXnW2u/bui/SaR1hrR0GnAxc4VkG0+Eo0O1ZFpDS6H4ykNNGfRER2SuedU1vAnOstfPauj/SeVlrS4CFwJg27op0HkcA4zzrnOYCxxljZrdtl6QzsdbmeL7mAW/hXmrV4SjQ7dlSoK8xpqcxJgA4F3i3jfskIrJHng0rngN+tdY+1Nb9kc7HGBNnjIn03A4GTgB+a9teSWdhrb3BWptsre2B+/evz621E9u4W9JJGGNCPBuGYYwJAU4COuSO4wp0e2CtrQemAB/j3lDgNWvtqrbtlXQmxphXge+A/saYLGPMJW3dJ+k0jgDOx/2p9g+eP6e0daekU+kOLDDGrMT9Aegn1lptLS8iHUEC8LUx5kdgCfCBtfajNu7TPlHZAhERERERkQ5KI3QiIiIiIiIdlAKdiIiIiIhIB6VAJyIiIiIi0kEp0ImIiIiIiHRQCnQiIiIiIiIdlAKdiIh0WsYYZ6OSDT8YY/7hxWv3MMZ0yJpFIiLSefi1dQdERERaUZW1dmhbd0JERKS1aIRORES6HGPMBmPMvcaYJZ4/fTzH04wxnxljVnq+pnqOJxhj3jLG/Oj5c7jnUr7GmGeMMauMMfONMcFt9qJERKRLUqATEZHOLHi7KZfnNHpsi7V2ODALeMRzbBbwsrV2MDAHmOE5PgP4wlo7BBgGrPIc7ws8Zq09ECgBzmjl1yMiItKEsda2dR9ERERahTGm3FobupPjG4DjrLXrjTH+wCZrbYwxpgDobq2t8xzPtdbGGmPygWRrbU2ja/QAPrHW9vXcvx7wt9b+u/VfmYiIiJtG6EREpKuyu7i9q3N2pqbRbSdamy4iIvuZAp2IiHRV5zT6+p3n9rfAuZ7bE4CvPbc/Ay4HMMb4GmPC91cnRUREdkefJIqISGcWbIz5odH9j6y1W0sXBBpjFuP+cPMvnmNXAc8bY64F8oGLPMevBp42xlyCeyTuciC31XsvIiKyB1pDJyIiXY5nDV26tbagrfsiIiLSEppyKSIiIiIi0kFphE5ERERERKSD0gidiIjsF8aYHsYYa4zx89z/nzHm/5pz7j609U9jzLMt6a+IiEhHoEAnIiLNYoz52Bhz+06On26M2bS34ctae7K19iUv9GuUMSZru2vfZa2d1NJri4iItHcKdCIi0lwvAucbY8x2x88H5lhr6/d/l7qWfR2xFBGRzkuBTkREmuttIBo4ausBY0wUcBrwsuf+qcaYFcaYLcaYjcaYW3d1MWPMQmPMJM9tX2PMA8aYAmPMeuDU7c69yBjzqzGmzBiz3hhzqed4CPA/INEYU+75k2iMudUYM7vR88cZY1YZY0o87Q5s9NgGY8zfjTErjTGlxpj/GmOCdtHn3saYz40xhZ6+zjHGRDZ6PMUYM88Yk+85Z1ajx/7a6DX8YowZ5jlujTF9Gp33ojHm357bo4wxWcaY640xm4AXjDFRxpj3PW0Ue24nN3p+tDHmBWNMjufxtz3HfzbGjG10nr/nNQzd1b+RiIi0fwp0IiLSLNbaKuA14IJGh88GfrPW/ui5X+F5PBJ3KLvcGDO+GZf/K+5geDCQDpy53eN5nsfDcdeGe9gYM8xaWwGcDORYa0M9f3IaP9EY0w94FZgKxAEfAu8ZYwK2ex1jgJ7AYODCXfTTAHcDicBAIAW41dOOL/A+kAH0AJKAuZ7HzvKcd4HnNYwDCpvx9wLQDXeQTgMm4/7Z/YLnfipQBcxqdP5/AAdwIBAPPOw5/jIwsdF5pwC51trGdfpERKSDUaATEZG98RJwljEm2HP/As8xAKy1C621P1lrXdbalbiD1DHNuO7ZwCPW2o3W2iLcoamBtfYDa+3v1u0LYD6NRgr34BzgA2vtJ9baOuABIBg4vNE5M6y1OZ623wN2OmplrV3nuU6NtTYfeKjR6xuOO+hda62tsNZWW2u/9jw2CbjPWrvU8xrWWWszmtl/F3CLp80qa22htfZNa22ltbYMuHNrH4wx3XEH3MustcXW2jrP3xfAbOAUY0y45/75uMOfiIh0YAp0IiLSbJ6Akg+cbozpBRwKvLL1cWPMCGPMAs90wFLgMiC2GZdOBDY2ut8k7BhjTjbGLDLGFBljSnCPLjXnuluv3XA9a63L01ZSo3M2NbpdCYTu7ELGmHhjzFxjTLYxZgvukLS1HylAxi7WEqYAvzezv9vLt9ZWN+qDwxjzlDEmw9OHL4FIzwhhClBkrS3e/iKekctvgDM800RPBubsY59ERKSdUKATEZG99TLukbnzgfnW2s2NHnsFeBdIsdZGAE/inqa4J7m4w8hWqVtvGGMC4f/Zu+/4qsvz/+Ov++xzkpOTTULYMlSQCiK4xQmWOjqUav221dZVEcTVYhUQW2u1de8627rHV0VELW4L/hSErxsHIoSEFcjJOidn3b8/PuecnJXkAAknCdezDx455zPOuU8omDfXfV83z2JU1vpprQsxpk3GXrezDVVrMKYnxl5PRd9rQxbjSvWX6PuN1VoXYExhjI1jPTConcYl64G92nnNFowpkjEVKedTP9+lwChgUnQMR0SPq+j7FCeu60vxSHTMpwLLtNY78z0QQgjRg0igE0IIsaP+CRyLse4tddsBN0aFyK+UmgickeVrPgXMVEoNiDZa+UPCORtgx6gMhpRSJwDHJ5zfBJQopTwdvPY0pdQxSikrRiBqBZZmObZEbqAJqFdKVQGXJ5z7ACOYXq+UylNKOZRSh0bP3Q9cppQ6QBmGK6ViIXMVcEa0McxUOp+i6sZYN1evlCoG5sVOaK1rMZrE3BVtnmJVSh2RcO/zwHhgFtFGNkIIIXo3CXRCCCF2iNZ6LUYYysOoxiX6HbBAKdUIzMUIU9n4B/Aq8H/AR8BzCe/XCMyMvtZ2jJD4YsL5LzHW6q2JdrHsnzLe1RhVqduBrcCJwIla60CWY0t0DUYg8gKLUsYZjr72cGAdUI2xfg+t9dMYa90eAxpp6xgKRrg6EagHfhE915FbMNYAbgXeB15JOf8/QBD4EqOZzMUJY/RhVDuHJo5dCCFE76W07mymihBCCCH6CqXUXGCk1vrMTi8WQgjR48kGpUIIIcQeIjpF8zcYVTwhhBB9gEy5FEIIIfYASqlzMJqmLNZav5Pr8QghhOgaMuVSCCGEEEIIIXopqdAJIYQQQgghRC/V49bQlZaW6iFDhuR6GEIIIYQQQgiREytWrNiqtS7L5toeF+iGDBnC8uXLcz0MIYQQQgghhMgJpdT32V4rUy6FEEIIIYQQopeSQCeEEEIIIYQQvZQEOiGEEEIIIYTopXrcGjohRN8SDAaprq7G7/fneihCCNFnORwOBgwYgNVqzfVQhBC7mQQ6IUS3qq6uxu12M2TIEJRSuR6OEEL0OVpr6urqqK6uZujQobkejhBiN5Mpl0KIbuX3+ykpKZEwJ4QQ3UQpRUlJicyEEGIPJYFOCNHtJMwJIUT36ot/zy5as4jjnzmesY+M5fhnjmfRmkW5HpIQPZJMuRRCCCGEED3KojWLmL90Pv6wUXWsba5l/tL5AEwbNi2HIxOi55FAJ4ToUZ5fuYEbX11NTb2P/oVOLp8yilPGVeVkLEOGDGH58uWUlpbu/jf/+Cl4fQF4q8EzAI6ZC2NP2/3jEDmxaM0ibv3oVjY2b6Qir4JZ42ftlh9iH374YZYvX84dd9zR7e/V1bwLF7L55lsI1dZiqaykfPbFeE48MdfDElnwhXxsbtnMpuZNbGoxfv3j43/Ew1yMP+xn/tL5rNy8Eo/dg8fmMb7aPRTYCpIe28y2HH0aIXY/CXRCiB7j+ZUbmPPcJ/iCYQA21PuY89wnADkLdTnx8VOwcCYEfcZz73rjOeQk1OU02O6kVatWUVNTww9/+MNcD2WHSWVix3kXLqT26rno6BqyUE0NtVfPBeiSUKe1RmuNydR9K1XC4TBms7nbXj9XmgJNRkiLhrWNLRvjjze3bGZTyya8rd6sX88f9vPq2ldpCDQQ0ZF2r3NanGkhLxYCC+wFacdiz/OseX1y+qro2yTQCSF2m2sWfsbnNQ3tnl+5rp5AOPk/0L5gmCue+ZjHP1iX8Z59+xcw78TR7b5mc3Mzp512GtXV1YTDYa6++mrcbjeXXHIJpaWljB8/njVr1vDSSy9RV1fH6aefzpYtW5g4cSJa6537oJ1Z/AfY+En756s/hHBr8rGgD16YASseyXxPxX5wwvVdN8ZebtWqVSxfvrxHBrq/fvBXvtz2ZbvnP97yMYFIIOmYP+xn7n/n8sxXz2S8Z+/ivfn9xN93+t6nnHIK69evx+/3M2vWLM4991weeugh/vKXv1BZWcnIkSOx2+0ALFy4kD/96U8EAgFKSkp49NFH6devH/Pnz+e7776jtraWr776iptuuon333+fxYsXU1VVxcKFC7u8df7G666j9Yv2v2e+//s/dCD5e6b9fmr/eBX1Tz2d8R77PntTceWV7b7m2rVrOeGEEzjqqKNYtmwZq1at4oorrmDJkiUUFRVx3XXXccUVV7Bu3TpuueUWTjrpJD777DPOOussAoEAkUiEZ599FqvVytSpU5k0aRIrV65k5MiR/POf/8TlcjFkyBDOPvtsXnvtNWbMmMHee+/N+eefT0tLC3vttRcPPvggRUVFTJ48mf33358PPviAhoYGHnzwQSZOnLhz38wuorXG2+qNV9Q2Nm+MB7TESltzsDnt3hJHCf3y+lGVX8X48vH0y+tHP1f0V14/yl3lnPz8ydQ216bdW5lXyWs/e42IjtAUbMLb6qUh0GB8bTW+egPRxwGv8bzVy/cN39PQ2kB9a33an69EZmWOh7sCe0HGKmCmoFhgK8Bikh+rRW7I//OEED1Gapjr7Hg2XnnlFfr378+iRcZieq/Xy5gxY3jnnXcYOnQop59+evzaa665hsMOO4y5c+eyaNEi7rvvvp1+312SGuY6O56F7gq2a9euZerUqRx22GG8//77/OAHP+Css85i3rx5bN68mUcffZSJEyeybds2zj77bNasWYPL5eK+++5j7NixWYeDFStWcMkll9DU1ERpaSkPP/wwlZWVTJ48mUmTJvHmm29SX1/PAw88wKRJk5g7dy4+n4/33nuPOXPm8MUXX5Cfn89ll10GwJgxY3jppZcAshr/7tTeD5sd/RCarQcffJDi4mJ8Ph8HHngg06ZNY968eaxYsQKPx8NRRx3FuHHjAOLfE6UU999/PzfccAN///vfAfj222958803+fzzzzn44IN59tlnueGGG/jxj3/MokWLOOWUU3Z5rDsiNcx1djxbq1ev5qGHHuKuu+5CKcXkyZP561//yo9//GOuuuoq/vOf//D555/zq1/9ipNOOol77rmHWbNm8Ytf/IJAIEA4HGbTpk2sXr2aBx54gEMPPZSzzz6bu+66K/7/RYfDwXvvvQfA2LFjuf322znyyCOZO3cu11xzDbfccgtg/BleunQp77zzDmeffTaffvrpLn22jkR0hG3+bcnhLCGkxR63pvydZFImSp2lVLgq2KtwLw7pf0g8pMXDmrMcq7nzwD9r/KykSjWAw+xg1vhZ8fcqsBVQYCvY4c/nD/njITAxAGY6VuevY413DQ2tDTQGGzt83XxrflvAi4bB1FAYO5YYCh1mh1QFxS6RQCeE2G06qqQBHHr9G2yo96Udryp08uR5B+/Ue+63335cdtll/P73v+dHP/oRbrebYcOGxfdqOv300+PB7Z133uG5554DYNq0aRQVFe3Ue3aqs0razWOMaZapPAPhrJ3r8tadwfabb77h6aef5r777uPAAw/kscce47333uPFF1/kuuuu4/nnn2fevHmMGzeO559/njfeeINf/vKXrFq1Cug8HEybNo2LLrqIF154gbKyMp588kn++Mc/8uCDDwIQCoX44IMPePnll7nmmmtYsmQJCxYsSFoLNn/+/F0af1fqrJJ2/DPHt1uZeGjqQ7v03rfddhv/+7//C8D69ev517/+xeTJkykrKwNg+vTpfPXVV4Cxh+T06dOpra0lEAgk7W92wgknYLVa2W+//QiHw0ydOhUw/rytXbt2l8aYSUeVNICvjz6GUE1N2nFL//4M/tc/d/p9Bw8ezEEHHQSAzWZL+px2uz3+PYh95oMPPpg///nPVFdX85Of/IQRI0YAMHDgQA499FAAzjzzTG677bZ4oJs+fTpg/Jmsr6/nyCOPBOBXv/oVp556anwssT+jRxxxBA0NDdTX11NYWLjDnykUCbHVt7XDsLbZt5lQJJR0n8VkiVfRRpeM5uhBRyeHNVc/SpwlXValik0v7o61pA6LA4fFQbmrfIfuC0VCNAWa4pW/zkLh1y1fxyuFqd/PRFaTNW3qZ2xaaEehMN+aj9nU96bpih0ngU4I0WNcPmVU0ho6AKfVzOVTRu30a44cOZIVK1bw8ssvM2fOHI477rgOr+8R/0p6zNzkNXQAVqdxfCd1Z7AdOnQo++23HwCjR4/mmGOOQSmV9IPue++9x7PPPgvA0UcfTV1dHV6vsW6ms3CwevVqPv300/jvXTgcprKyMv7+P/nJTwA44IADdipMZDP+3amzysTOeuutt1iyZAnLli3D5XIxefJk9t57b7744ouM11900UVccsklnHTSSbz11ltJoTg2LdNkMmG1WuN/bkwmE6FQ+z+4dpfy2RcnraEDUA4H5bMv3qXXzcvLiz9O/ZyJ34PYZz7jjDOYNGkSixYtYsqUKdx///0MGzYs7e+VxOeJ79GRjl4jRmtNdWN15pDWspmNLRvZ6tuatvbMYXbEg9kB/Q6IPy53lccfFzuKMandu9vVtGHTetS6UYvJQqGjkELHjgVprTW+kC85BCZMB41PGY1+3diykdXbV+Nt9dISamn3dRUKt82dcY1gYgUwUyiUpjF9iwQ6IUSPEWt80pVdLmtqaiguLubMM88kPz+fu+++mzVr1rB27VqGDBnCk08+Gb/2iCOO4NFHH+Wqq65i8eLFbN++fZc/006JNT7pwi6X3RlsYz/YQvs/6Gaathl7j87Cgdaa0aNHs2zZsg7f32w2txsmLBYLkUjbD7GJGzBnM/7dqbsqE16vl6KiIlwuF19++SXvv/8+Pp+Pt956i7q6OgoKCnj66af5wQ9+EL++qsr4s/fII+2s3ewhYo1Pct3lcs2aNQwbNoyZM2eyZs0aPv74Y4YNG8a6detYtmwZBx98MI8//jiHHXZY+mfweCgqKuLdd9/l8MMP51//+le8WgfwxBNPcMjhh/D2u2+TX5BPwBqgpqmGUCREMBIkGAlS21zL9OemJ71uvjU/Hs7SpkC6+lGRV0GBraBn/GNWH6WUwmV14bK6qKSy8xsSBMNBI+gFOl4j2BBooKG1gQ1NG+LPO2saEwuDiZXBxLWBmSqD0jSmZ5JAJ4ToUU4ZV9WlHS0/+eQTLr/88nhYuPvuu6mtrWXq1KmUlpYmrY+aN28ep59+OuPHj+fII49k0KBBXTaOHTb2tC7taJnrYBt7zauvvpq33nqL0tJSCgqyW/syatQotmzZEv+BOBgM8tVXXzF6dPtTeN1uN42NbetdhgwZEl8z99FHH/Hdd9/t2gfqZt1RmZg6dSr33HMPY8eOZdSoURx00EFUVlYyf/58Dj74YCorKxk/fjzhsFEhnz9/PqeeeipVVVUcdNBBPf575jnxxJxvU/Dkk0/y73//G6vVSkVFBXPnzqWhoYF99tmHRx55hPPOO48RI0ZwwQUXJN0XjoQJRoLcff/dzLxwJi0tLQwaMogb7ryBb+q/oSXYQtgRZtIhk2hubObaW69lc8tmzCYzFpMFq8mK0+KkwdbAgkMW0C+vHxWuCspd5eTb8nP03RBdwWq2UuIsocRZskP3RXSE5mBzWgCMh8KUauH3Dd/HQ2HqdhGJzMqctE4wU6fQTA1jCuwFWE1d2yxJtJFAJ4To06ZMmcKUKVOSjjU1NfHll1+itebCCy9kwoQJAJSUlPDaa6/Fr7v55pt361i7U66D7fz58znrrLMYO3YsLpdrhyo+NpuNZ555hpkzZ+L1egmFQlx88cUdBrqjjjqK66+/nv333585c+bw05/+lH/+85/sv//+HHjggYwcOXKXP1NvY7fbWbx4cdrxyZMnc9ZZZ6UdP/nkkzn55JPTjqeuR2xqamr3XG82ZMiQpMYjHX3O2Lk5c+YwZ86c+HGtNdvqt6FMihtvuzFeSdse3s5m72b+s/I/bIlsYdO2TQB4hnp45GXjz4bFZImHNYvJwvRTp/O3v/4Nq9kaP546BdJr8/LjET/u0u+D6J1MyoTb5sZtczOAATt0b6xpTKYqYOrawe3+7az1rsUb8NIY6LhpTJ41L2n6Z0fbRySGQqfF2S1VwVzt99kdVLe15d5JEyZM0MuXL8/1MIQQXeSLL75gn332yfUwktx888088sgjBAIBxo0bxz/+8Q9cLleuh7XbNTU1kZ+fHw+2I0aMYPbs2bkelhC9gtaakA4RCrdNeYxNf0ycBln9fTUX/uJCnn+3rbmOxWTBarbGw5rVlPzYYrIkhbXJkyfzt7/9Lf6PT+3piX/fij1HOBKObyXR2dTQxGPZNI3JtCawvX0GY8/dNne7TWNS9/sEYy3p/EPm95hQp5RaobXu+A997FoJdEKI7iQ/YPRcEmyFyExrnRTK0h6Hja+a5J+hlFIZA1pqWOuuNUjy963ojWJNYzJVAFM7iiZNHQ14M+5zmMhtc2es/C1as4gfrPJyxluakgaoK4DHJivWTBzAaz97rcPX3F12JNDJlEshhNhDzZ49O+uKXF1dHcccc0za8ddff52Skh1b2yFELkV0JGMlLbXSlkopFQ9nLqsrOahFq21mZZaGEV3Iu3BhzhvdiO6X2DSmIq9ih+4NRoJJ20UkhsHUjqINgQZqm2vxtnr5wSov572scUT/qJc1wHkva+5jA/ysGz5kN5NAJ4Todlpr+SGnlyspKYnvGydETxWOhDOHtYTH4Ug47T6TMhkBzWzFbrFnrK719LDW02Zc7SrvwoVJW1GEamqovdrYukVCXRutNUQiEA4bj8NhdERDJAyRCDoSMb6GI5mP6Qg6HIb27k04Frsv7d74+ejXcPSaSOK5CMTvSfiael3Ge6Nj0ymvEbsuHMGmI5SGI5Qmfr7Ya8TvtUCkEB0poOH/tmBN+avAEYIz387N7+OukkAnhOhWDoeDuro6SkpKevQPQ0KInktrTURHOgxqoXCIsE4Pa2aTOR7KnBZnUlCLh7Vevjmz1pq6ujocDkeuh7LLdDBIuKmJzTf+LWlfQQDt97Ppur+gzObkkJEWFMLZhYfUexNDTHvhoZ2QkR4eIsnhKVPIyBg8OnmNcHIoo48FecxmlMkEJpPxWKmkr5hM0fPKOKYABUpp4ysalAY0iggQiX9Fh1GEUTqMJayNG1MUedvf6qEnk0AnhOhWAwYMoLq6mi1btuR6KEKILhZb9xKOhDGbzPGOdDsqoiOEdZhwJGx81WEikUj8cViHM1agTMqE2WTGrIxfJmUyHicc00oTiP6vL3M4HAwYsGPdDLuaDoeJNDURbmwi0tRIpLGRcGPsq3Es3BB93tRIpLEp+ZqmJrTP1+F7hLdvZ8Mll3bNgJMCQvrXxHDRdsyEUrFzCkwZXsNsNgKHyYyyWDq517gu8Vz8WPyrCWU2gYq+hsmUfm/0WNrrtnNv7JgyR8dmSnlds7ntfOyr2Qwq5VzKvcoUDV7x8wnHUKhIAILNqFALBJtQwWYINRvHAk3Q2gCtjRl+pRwPtb+1QhsF9gKwu6O/CuOPv75+KaGW9Bhkde3+vUe7ggQ6IUS3slqtDB06NNfDEEJ0sUVrFjF/Rcdd4iI6wjb/NjY1b2Jjy0Y2NW9iU0v0V3Pb10AkOWyZlIkyZ1naBtiJm2KXOcuwmmVfq66iIxEiLS1tAaupiXBDgxG6mqKBrLEhGr6ajEDW0JhwrpFIc8cNKgCU3Y7J7cacn4+poABzfj6WigpM7nzM7gLja76bLXfdRaS+Pu1+c1kZgx96sJ3wkBC8EsJVvMqTWvkR2YmEIdAErU3pAaulg9CVKZBlqKKnMVnBkRjECsBdCaUjE45Fj9vyk58nnrflQTu/z+Vv7Uvt22F0uK2brDJHKD+od/6dIoFOCCGEEJ3SWhOIBGgJtuAL+fj78r+nbUDsD/uZv3Q+T3z5BJtaNrGlZQshnfwv3haTJR7SxpSO4dhBx9Ivrx/lrvL48RJnCRaT/IiSLa012udLqII1EGlq6rAyFm5saKuQRa/tdPqe1RoNYm7M+W5Mbje20hJM7gLM7nxM+e5oMDPOmd1uTPlu41w0vCmbLavPZC4qTFpDB6AcDvpdcTn24cN35du15wgFokEsU9DK9lij8RrZsOYlBKpo0MobmhLC2glficcs9u79vgCe8+dD66VsXukg1GLG4gpTPs6P5/y/d/t7dwf521IIIYToQyI6gi/kwxfyxcNXS6gFX9DX9jj1XKbrMxyL6M7Xl/jDfmxmGxP6TUiqsMUeFzmK0jbE3tNFWltTpic2tlXGUqpgscpY0pTFpiYIdTJVzGSKB61YhcxaVZVwzKiMmQpSglgsmLndKLt9t1W2Yo1P9rgul1pD0LdzwSvHOJlaAAAgAElEQVTxeKBpJ6clusFRCJ6BnQevpGpYPph7UawYexqeWeB5fQF4q8EzAI75M4w9Ldcj2ymyD50QQgiRA8FwsC1cRQNXu8+DCQErIZxlCmipVbPOOMwOnBYnLqsLp8VpPLZEH1sTHidc47K4uGnFTdS3pk+Jq8yr7DH7OO0OsSYekcbE8BULWw2dBrFIQwM6GOz4TZTClJ/fFroSQlasMmYucCdXyGLHouFNuVwyzbA7xacl7mDwyjSNcWenJWZTCbOlBrH2pyWK3JJ96IQQQoguENvwNlNwarfa1U4lLKnyFfSlTUXsiEmZkoJWLFi5bW7KXeVpYStjQMsQzhxmx053eLSZbcxfmr6Gbtb4WTv1ernQ1sSjMWntWHI4S23ckTxlMbUTYyYml6utCuYuwFxchG3QoLZAFls75nZjys/HXFCQVCEz5eUZ67/2NB8/BUkVlLldX0EJBbKvhHXUtGOnpiVGf+WVJgSvDtaE7eZpiaL3kEAnhBCi1wtGgvGgtKPTCjNVv2Ln/CE/muxnsthMtozBqcxV1n7QyhDGko5ZXdhMth5XXYk1Prn1o1vZ2LyRirwKZo2fFT/e3eJNPBoa2taJZZqy2JhSGUtYTxZpaen0fZTDkTwdMd+NtbJ/PIi1u3Ys1vgjPx9lkR+3dtjHT8HCmcb0QwDveuM5wH6nQrClg0rYDnRLDLdmMZg9dFqi6DXk/1VCCNGHLVqzKGc/cKfSWtMabs0YnLIKWh0EtGCkkylrCRQqHppSw1SJoyQ5cHUUtDIEtD2tkcdhn0UYeVeYUG0IS2WY8tkRGNb5fW1NPJIrY5m7KjZlbubR1NRpEw9ltSYFLJM7H1tpaVJTj3ggS107VlCAOS8v6yYeIkuRCAQawd9ghCu/N+Wx13j84f1tYS4m6IPnzoX/PQ+yWM+ZcVpiQf8da9Bhd4PVJdMSRY+W1Ro6pdRU4FbADNyvtb4+5fz5wIVAGGgCztVaf66UGgJ8AayOXvq+1vr8jt5L1tAJIUTXWLRmUcYpcYlt5TMJR8LpUwS7qMFGNk01YmIbQe9QZcva+fUOs6PHVbt6A601BINE/H4iPj8NLy9iyy23olsTKhxWK+4pU7APGpjQVTF5ymK8iUe4k3VCZnN6w45s1o4lBji7TEvrUlpDoDlzEEs7lng88bpG6KzqbbZBuIN9Aw+/rJ31YjItUfQdO7KGrtNAp5QyA18BxwHVwIfA6VrrzxOuKdBaN0QfnwT8Tms9NRroXtJaj8l28BLohBBi5wQjQbytXur99dS31nPJW5ewvXV72nUOs4ODKg9qtxLWmtUUpDaJ1a7E9Vo7UtlKO2dxyR5jO0BrjQ4E0D5fNHD5kh9HQ5j2+4j4Yseij/0+tM8fvbYl/jh+f+y8z9d5CItRqm3KYcLasaRgVpDcSTF17ZhyOiV4dyWtja6H/pQA1lH48jdAqzfhfBYNO5Q5WhUrAIfH+GUvMI4lPk465km+x+qAm8cY0yxTeQbC7E+753skRA/S1U1RJgLfaK3XRF/8CeBkIB7oYmEuKo9O/+lFCCFEe7TWtIRaqG81gpnX740/TvzlbfUmfW0Odr6pLxht5Te1bMJpcVJgL6Air6Lj5hkpxxKfOywOaUHfCR2JoFtb20KSz5ccrvzRwNWSOWRpv49Iiy9zyEoIb53uIZZKKZTTicnhwORwGI+jz80eD6Z+/VBOByaHE5Mzej722OFk47x57b7u3p99umc28ehOoUD6tMSMQawB/PWZw1mnU5NVQhCLBqyCKijfN7sg5ijouumJx8xNXkMHYHUax4UQSbIJdFVA4j+RVAOTUi9SSl0IXALYgKMTTg1VSq0EGoCrtNbvZrj3XOBcgEGDBmU9eCGE6OlCkRDeVm88dCUGsI4ehyLtd0B029wU2gsptBdS7ChmmGcYhfZCPHZP/LjH7uHK965kq29r2v2VeZU8deJT3fmxew0dDkerVymBKaGqFfG1pFe4EqpWSeErQ4Usmy6IaUwmTE5nW+BKeGwuKcbqMB4rV3LIMoJZNIS5nEZDj+h9Knpd7LWUbdcarWy9915CNTVpxy2VlRLmUoVD6RWxjtaPZZq2mM2eYjZ3cujKK4OS4dkFMXuB0bSjp/zexbpZdneXSyH6gGwCXaa/7dP+GVBrfSdwp1LqDOAq4FdALTBIa12nlDoAeF4pNTqloofW+j7gPjCmXO7gZxBCiG4Xa1+fGLoyVcsSK2reVi+NwcZ2X9NisiQFsCEFQ9JCWaG9kEJH2+MCW0HWjTcum3BZr24rr0OhaDUrw5RBfzQwJVWz2ipdaRWu2OOWlmiFywhfOtDBOp32WK1tVa1YoIpWuMxutxGonK7kcOVMCVTxYwmBy+kyzjscYLX2+OmG5bMvpvbquUmBVTkclM++OIej6gaRSLQzYjtBK5v1Y9lUz62ulCpYIRQO6iCIZZjCuJNbUPRYY0+TACdEFrL5qaAaGJjwfACQ/k9ybZ4A7gbQWrcCrdHHK5RS3wIjAVkkJ4TImXAkTEOgIetqWSygBSLt//Cfb81PCmODCgalBzN7IR5H22OXpXs3+p02bBp5b67Aet9TFHrD1HvMBM89kcm72OVSa40OBtsqUN2ybssPnW22nIGy2ZKmDrZVuByYi4qi1anEkGU8joewhGPxEOZKeC27HWWVtX0AnhNPBGDzzbcQqq3FUllJ+eyL48d7BK2N/cE6DGKdVM6yauJhT694uSuixzzJVbC0IBY9J2tGhRA7KZtA9yEwQik1FNgA/Bw4I/ECpdQIrfXX0afTgK+jx8uAbVrrsFJqGDACWNNVgxdCCH/In14t86cHs8SA1hhobHdvMYuyUGAviIeuAfkDGFMyJl4pyzS10WP3YDX1vB/G6l9cSMXtz6H9RhODYm8Ydcsz1FaDY999MkwZTAhZfh86tm4rscIVDVxZN8dIoOJTBx3JVa18N6by8vQpg/GqVnKFK17VSqh0GVMKHShzH6tQ9HCewT48J24Cbw14TDDY1/lN2dLaWD+VFLTqO+miGDsWC2RZNPEwWdKnHhYPSz/WXhCzFxhNPIQQIkc6DXRa65BSagbwKsa2BQ9qrT9TSi0AlmutXwRmKKWOBYLAdozplgBHAAuUUiGMLQ3O11pv644PIoTo3SI6QmOgMb1C5m8/mHlbvUnTCVO5LK6kAFaVX2U8bieYFdoLybfm9/ipbjoSIbx9O6EtWwht3mx8TXgcjB2rqU2/Nxik/vHHkw8qlbRGKzFwmYsKMTkqO12jlVTVylAhU3a7rKvqazra+HnsaRBq7WR9WBZt7ztr4qFM0U2eE6YjegaAY3TnQSx2zOqUPcaEEL1aVvvQ7U6ybYEQvV9ruDUtiLX3OPa1IdDQ7h5lJmXCY/N0uL6syF6Udt5m7l0bAutwmPC2bcmhLB7YtrY93roVQulNU8weD5byMixlZVjKyvG+8ELmN1KK4W++EQ9cu9ocQ/Qx4aBR2Ur71ZD8/P27jOmMqZTJ2EcsmyYe9oKUdWMpFbHOWt/b8iWMCSH6pK7etkAIsYfSWtMYbExrm99RMKtvrccXan/aldPijAcvj91DRV5Fu9Wy2GO3zd2rW+PrUIhQ3baM1bSkY3V1GacymouKjJBWXo59+PD4YyO4xR6Xpm2i3Pzhh+12IbRWVHTb5xU5ENtjrL3wFT/W1HFIa22EDv78tlG0u65MR2DiuZ3vQWZ3970mHkIIkQMS6ITYQwTDwU5DWWpA87Z6Cbez/kSh4mvNPHYPZa4yRhSNaDeYxZ47LH1nrYkOBgnV1WUMZ20Vti2E6+rS9whTCnNxcTSQlWHfe1RSSLPGHpeWomw7V2ncY7oQ9maRiFHlSgxUgSyqY5mOd7DVRVxsvZjd3fY1v5/R2t7uTj6e9CvxWD5Y8+DWse1v/Hz8tV3/vRJCCJGRBDohcmzRmkXc+tGtbGzeSEVeBbPGz2JaB10ItdY0B5vTgli7IS069bEl1NLua9pMtvi6skJ7IcMLh7dbLYs9dtvcmPvov65HAgHCsWCWVE3bklRhC2/fnh7UTCZjn7Cycqxl5ThHj8ZSVt42FTIW1EpKur1bYq/oQthbZTstsbPjgfa3tUhidaWHrKIhxldbfgfhK+WYxd51UxRl42chhOgRZA2dEDm0aM2itH3CrCYrxww6hsr8SiOUpTYFCXiz3nS6o2mMiY+dFucesYYq0traFsw6mP4Yrq9Pv9lsxlJS0hbIkqY8GmvWjKBWjLLIv5X1SFlPS+wskDVlPy0xLVhlGbwSf9ncYO6h/5/6+CnZ+FkIIbrBjqyhk0AnRA4d/8zx1DandyKE5E2nOwxlO7npdF8S8fk6XJtmTH/cSsTrTb/ZYmkLZvFwljLtsbwcc1GRtMTPlUzTErMJY/F7umBa4g6FsWjFzJoH0tlTCCHETpCmKEL0Eu2FOYXiozM/2iOqZh2JNDcnr0dLnPaYcCzSmD5tTVmt8WBmHzqMvImT2ippCdMfzYWFfbudfi4rKPFpibvQoKMrpiWmTUncjdMShRBCiG4mgU6IHFlWs6zdcxV5FX02zGmtjaC2uZ1OjwnHIi3p6/6U3d4W1EaMIO+QQzJMfywzglof/R5mrbN9wjKJb+a8iw06Whuza1ufaVqiowA8VX1nWqIQQgjRjeS/fkLkwPKNy5n5xkwqXBVsb91Oa7g1fs5hdjBr/Kwcjm7naK2JNDZ2vNF1tMKmfenrj5TT2dbxcZ+9yTvi8LYpjwlr1kwFBRLUsvX6guSGFWA8X3gxrF7cfiBrp7NpkozdEiugZEQnVbCUXzItUQghhNglEuiE2M1WbV7Fha9fSP/8/jw45UHer31/h7pc7m5aayJeb1Ib/vbWq+nW1rT7TS5XPJA5R4/BclR5ciORcuOcKS9PglpXqV8Hq1/J3FIeINgMGz9um4oY65Yo0xKFEEKIXkeaogixG3229TN++9pvKXYU8/DUhylzleVsLDoSIVxfn971MbXCtnUrOhBIu9+Un59hc+vkpiKWsnLM+Xk5+HR7mEgEalbCV4uNytumT43jJkvmJiCegTD70907RiGEEEJkTZqiCNEDrd62mnP/cy4eu4cHpjzQbWFORyKEt23rcKPr0JYthLZuhWAw7X6Tx4OlrBRLWRmuAydkCGzRqY9OZ7eMX2Qp0AJr3jJC3FevQtMmUCYYeBAcdy2MOsEIebJPmBBCCNGnSaATYjf4tv5bznntHFxWFw9MeYCKvIr4Oe/ChVlt/KzDYUJ1dSlTHTNMf9y6FcLpa6DMhYVtzUSGDUuvqpWXYyktxeRwdOv3QuyChlr46hXj15q3jKYjNjcMPwZG/RBGHAeu4rbrS0cYX2WfMCGEEKLPkimXQnSztd61nPXqWSgUD019iMEFg+PnvAsXUnv1XLQ/oRug1Ur+kUdiKSpKnv5YV2dMrUthLi5uZ6Prtn3UzGVlmGy23fFxRVfSGjZ+YgS41S8bFTeAwkEw8gSjCjf4ULDI760QQgjRl8iUSyF6iPWN6/nNa78hoiM8NCU5zAFsvvmW5DAHEAzStGQJ5tLSeDCz77tPxo6PlpISlAS1viXUCt+9G10P9wo0VAMKqg6Ao682Qlz5vtKQRAghhBCABDohuk1tUy2/ffW3tIZbeeD4BxhWOCzpvA4GCdXUZL5ZKUa+9+5uGKXoEZq3wtevGVW4b9+EQJOxSfawo2DyH2DkFMgvz/UohRBCCNEDSaATohtsbtnMb177DY2BRu6fcj+jikclnQ9t28aGWRe3e7+lsrK7hyhySWvYsrqtK+X6DwAN7krY71SjCjf0CKOBiRBCCCFEByTQCdHFtvq28tvXfkudr45/HP8P9i3ZN+m8//PPWT9jBuG6bRSe/nO8//t80rRL5XBQPrv9sCd6qXAQvl8aXQ+3GLZ/ZxyvGAtH/h5GTYXK/WUqpRBCCCF2iAQ6IbrQdv92znntHDY2b+SeY+9hbNnYpPPelxZRe9VVmIuKGPzoozjHjMY1fnxWXS5FL+TbDt+8bkyl/HoJtHrBbDeqb4fMgJFTjc6TQgghhBA7SQKdEF3E2+rlvP+cx/rG9dx5zJ2M7zc+fk6Hw2y5+Wbq7n8A54QDGHDrrVhKSgDwnHiiBLi+pO7btirc90tBh8FVCvucaFThhh0F9vxcj1IIIYQQfYQEOiG6QFOgiQuWXMA39d9w29G3MalyUvxc2Otlw2WX0/zuuxSdcTr9/vAH6UzZl0TCUP2hUYVb/QpsXW0cL9sHDp1lrIerOgBM5tyOUwghhBB9kgQ6IXZRS7CF373+O76o+4KbJt/EYVWHxc+1fvMN6y+8kGBNLRXXLqDo1FNzOFLRZVob4ds3jCrc169BSx2YLMaecBPOMqZSFg/N9SiFEEIIsQeQQCfELvCFfMx4YwYfb/mYG464gaMGHRU/1/j669RcfgXK5WLwI4/gGj8uhyMVu6x+fdtUyrXvQjgAjkIYcbwxlXL4seDw5HqUQgghhNjDSKATYie1hlu5+M2LWb5xOX85/C8cP+R4AHQkwta77mbrHXfg2G8/BtxxO9Z+/XI8WrHDIhGoXWkEuNWvwKZPjOPFe8HEc42plAMPArP8NSqEEEKI3JGfRITYCcFwkEvfupSlNUtZcMgCpg2bBkC4qZmaP/yepiWv4znlFCqumY/Jbs/xaEXWAi3w3dtGiPvqFWjaBMpkBLfjFsCoH0LpiFyPUgghhBAiTgKdEDsoFAlxxTtX8Hb121x90NX8eMSPAQh8/z3VM2bQuuY7+l05h6L/+R+U7CnW8zVujE6lfAXWvAUhH9jcMPxoI8ANPw7ySnI9SiGEEEKIjCTQCbEDwpEwV753JUvWLeH3B/6e00adBkDTe/9lwyWXoEwmBj1wP3kHHZTjkYp2aQ2bPo1OpVwMNR8Zxz2DYPz/GFMpBx8GFulEKoQQQoieTwKdEFmK6Ajzls5j8XeLmX3AbM7c90y01mx78CE2//3v2EeMYMCdd2AbIBtF9zihVqORyepXjGqcd71xvGoCHH2VUYkr3xekoiqEEEKIXkYCnRBZ0Frzp/f/xAvfvsDv9v8dZ485m4jPR+3Vc2l46SXcU6fS/7o/Y3K5cj1UEdNcB1+/alThvn0DAk1gccJeR8GRV8CIKeCWZjVCCCHEnuj5lRu48dXV1NT76F/o5PIpozhlXFWuh7VTJNAJ0QmtNX/98K88/dXT/Ha/33L+2PMJ1tSwfsYMWr/4krLZsyk59xxZL5drWsPWr9qmUlZ/ADoC+RWw38+MKtzQI8DqzPVIhRBCCJFDz6/cwJznPsEXDAOwod7HnOeMbta9MdRlFeiUUlOBWwEzcL/W+vqU8+cDFwJhoAk4V2v9efTcHOA30XMztdavdt3wheheWmtuXnEzj37xKL/c95fMHDcT3/LlVM+6GB0IMODuu3BPnpzrYe65wkFYt8yYSrn6Zdj+nXG8Yj844nJjg+/K/cFkyu04hRBCCJETWmtaAmG8viD1LUHqfQEWLPwsHuZifMEwN766um8GOqWUGbgTOA6oBj5USr0YC2xRj2mt74lefxJwEzBVKbUv8HNgNNAfWKKUGqm1Tv4OCtFD3bnqTh767CGmj5rOpQdcyvbHH2fTdX/BNmgQA+64A/uwobke4p7HVw/fLDGqcN/8B/xeMNuM6tshM4wQ55F1jEIIIURfEo5oGnxB6n3BaDgLxENaYljztiReE8TrCxAM66zeo6be182fontkU6GbCHyjtV4DoJR6AjgZiAc6rXVDwvV5QOy7djLwhNa6FfhOKfVN9PWWdcHYhehW9318H/d+fC8/GfET/jDuMjbNm0f908+QP3ky/W+8AbPbnesh7jm2rWmrwq1bBpEQuEph7x8ZAW6vo8Auvx9CCCFET+cPJlTLYqHMF4wGsfZDWoM/1OHr5tsteJxWCl3Gr34F+XicNuO50xo/53HamPXESjY3tqa9Rv/C3rksI5tAVwWsT3heDUxKvUgpdSFwCWADjk649/2Ue9PqmEqpc4FzAQYNGpTNuIXoVo989gi3r7ydE4edyJV7XUD1r87Ct2oVJeefR9nMmSiZwte9ImGo/rBtg+8tXxrHy/aGQy6CkSfAgAlgMud2nEIIIcQeKBLRNLaGjIpZNHTFQlisehYPZQlhrb4lSGso0u7rmk0qHr48Liul+TaGl+cbzxPCmvG8LawVOK1Yzdn/bHblD/dJWkMH4LSauXzKqF36vuRKNoEuU6eHtLql1vpO4E6l1BnAVcCvduDe+4D7ACZMmJBdTVSIbvL4l4/zt+V/Y8qQKVyZ/zPWnTqdcFMTVbfcQsHUKbkeXt/V2mR0o1y92OhO2VIHJgsMPgQO+LVRiSuWKa5CCCFEVwmEItEQFkiomrVNWfS2BKhPqJglTnWMdPATu9NqTgheVoaUuih0FlLoMsKXEcRsSdcUuqzk2y27pclcbJ3cntTlshoYmPB8AFDTwfVPAHfv5L1C5NQzXz3Ddf/vOo4aeBRzth5E9YxfY+nXjyGPP45j1MhcD6/v8Va3daVc+y6EA+DwwIjjjQA3/FhwFuZ6lEIIIUSPFWv6UR8LWwmVsVj1rCElrDVEr20OtN/WQikocFjbpiy6bAwsdlHotKYEsYRpjdHjdkvPn0FzyriqXhvgUmUT6D4ERiilhgIbMJqcnJF4gVJqhNb66+jTaUDs8YvAY0qpmzCaoowAPuiKgQvR1V789kUWLFvAkRWH8oelZWz+99W4Dj6IqptuwlJUlOvh9Q2RCNSuik6lXAwbjRbBFA+DiecaIW7QQWC25nacQgghxG6W2PSjPloZ82ZYS+ZNuCZ2LtRBucxmNsUDWKHLSlWhk9H9C5LXlblsSc8LnTbcDgsmk2zJ1Bt0Gui01iGl1AzgVYxtCx7UWn+mlFoALNdavwjMUEodCwSB7RjTLYle9xRGA5UQcKF0uBQ90SvfvcLV/72aI93jueTRFrwfPEbxr39N+WWXoiyyXeMuCfpgzdtGQ5OvXoWmjaBMMHASHHuNsT9c6QjjnwKFEEKIXs4fDCc09QhkbPiRFNaia8saO2n64bZb8LjaAldFRYHx3JlhXVkswDltOKwm2Su3j1Na96wlaxMmTNDLly/P9TDEHuT171/n0rcv5bjgCM55bBuRLVupvHYBnpNPzvXQeq/GTUYzk69egW/fhJAPbPkw/BijocmI4yGvJNejFEIIITKKNf3IHMQyN/yIXdNR0w+LSbWtI4tNV3QmriszjsWagsSeFzgsWHag6Yfo/ZRSK7TWE7K5VkoPYo/2TvU7XPbOZZy6vpKfPv01qrCIwY8+inO/MbkeWu+iNWz6rG0q5YYVxnHPQBh3Jow6AYYcBhZ7bscphBCi13h+5YZdblqR2PSjs4Yf9QnHGjpp+uGymePryjxOC8NK86NTF9sqY4kdGI2qmY08m1mqZaLLSaATe6ylNUu55PWLueD/uTn8jbU4xo9nwG23YiktzfXQeodQK6x9r21rAW90d5OqA+Coq4wQ12+0TKUUog/rih+4hcjk+ZUbktrKb6j38ftnP6a6voXxA4uSGn54Y1Wy+PNQPJi1dND0w6SIV8pia8gGF7uSmoB4nG1TGmN7mHmcVmwWqZaJnkOmXIo90ocbP+Syly5g9kLFqNVNFE6fTsUfr0TZbLkeWs/WXAdfv2ZU4b55HQJNYHEaG3uPnAojp4C7ItejFELsBqk/cIPRqvwvP9mvT4U6rTURbTSsiOjYL+O51jp6nPg547hxPqxj15B8LvY6kZTXSrk/9f3izyOkvXYkcSzRa41rEu6NvV/C+yfeH4m+XvafK/2a5PdPea3ove199sR761uC6ftctcNmMVGUUBlLW1cWDWuFKde47dL0Q/RcMuVSiA6s2ryKPz1xPtc+E6SsXlMxfz5FP5+e62H1TFrD1q+jDU1egfX/D3QE8itgzE+NhiZDjwCbK9cjFULsRo3+INe9/EVSmAPwBcNc/cKnrK1rzhge2vsBvqPw0F6oySY8pIaa9sJDetAi6d6+QikwKYVZKZQyNnFOfGxSCpNJYVJEj6vocTBFr026xhS9Jvq6seNWq8l4nnh/wvOk91MKs4m0935k2fftfo4nzz0ovs6s0GXFYe35LfKF6E4S6MQe5dOtn3LPnb9l/vM+nHmFDHrkdlwHHJDrYfUs4RCsW2YEuNUvw7Y1xvGK/eDwy4yplJX7g0mmmwjRF/mDYWq9fmrrfdQkfvX6qKn3UVvvp7G1/W58jf4Qtywxdi/K5od5U1KYIC00pAWD6LXm6PUWi6ndYNF2POG9M4SStLF0EGrSAlHCeyRdYyLhdY3naWPJ8N5J7xd/nHx/7HtlSvlcme5P/N71prVbS77YzIZ6X9rxqkInk4ZJUy0hEkmgE3uML7Z+zotX/5KL3vJh3XskQ++6B2tlZa6H1TP46uGbJUaI+/o18HvBbIMhh8NBvzOmUxYOzPUohRC7KBSOsKmxNTmsJQS22no/dc2BtPtK8mxUFjoYXJLHwcNKqCx0cu/b37K9JZh2bf9CB+9dcbRMZRO75PIpozJO6b18yqgcjkqInkkCndgjfLXhYz6a8T/8+IsA1h8ey7DrbsTkcOR6WLm17bu2Ktz3SyESAlcJjJpmVOH2Ogrs7lyPUgiRpUhEU9cciFfSauqjVbV4cPOzudGf1rnPbbdQWeig0uNkv6pC+nscVBY6418rPY6MU9oqChwZf+C+YsreEubELoutw5SmO0J0TpqiiD7v28+W8t0F51G5JYRj1nkMO29Wr5p20mUiYahebjQ0Wb0YtnxpHC/b26jAjToBBhwIJlmLIERPo7WmwReixuuLBra2itqGeh+1Xj8bvX4C4eT9r+wWE/2joazS46R/YdvX2HG3w7rT45Iul0II0T12pCmKBDrRp3235AXqLp+DBopuuJbhx/0010PavVqb4Ns3opt8vwotW0GZYfAhRkOTUVOheBbEbaEAACAASURBVFiuRynEHs8XCBthrd5PTcJaNSPAGRW25pT262aToqLAYYS1wmhI8xghLRbWivNse+Y/YAkhRC8nXS7FHk9rzdp7b6Pl1nuoLzMz6K67GT7m8FwPa/fwbmirwn33DoQD4PDA8OOMKtzwY8FZmOtRCrHHCIYjbPT6jZDm9ceDW6zSVuP1UZ9hLVppvp3+hQ6Gl+Vz+IhSI6xFK2xVhU7K3HbMMrVRCCH2eBLoRJ8T8fv57sorCLz8Hz7ax8oBtz3EyIF9uJNlJAK1q6Lr4RbDxo+N40VD4cBzjCrcoIPBvPPTqoQQmUUimi1NrW1hLeFrbO3alqbWtNb3Hqc1XkkbN6iQ/oUJ0yE9Tvp57NgtMv1ZCCFE5yTQiT4lWFvL2t9dQODL1fzvUXZOuuYR9i3/Qa6H1fWCPqP6tvplYyplYy0oEwyYCMdeY1TiSkcamw4JIXaKjm5unDwVsm3tWo3Xx6YGP8FwclpzWs1URqc/jhpVlrZ2rdLjJM8u//kVQgjRNeS/KKLPaFm+nPUzZ9LS7OXu05yc/7v7GduXwlzjJvj6VaMK9+2bEPKBLR/2OtoIcCOOh7zSXI9SiF6juTVErdfHhvrkPdfaKmw+/MHkJiNWs6JfgVFZmzC4qK0bpMcZr7J5nFZZtyaEEGK3kUAn+oTtTzzBxj/9ia1FZm44y87V0+9hfL/xuR5Wdj5+Cl5fAN5q8AyAY+bC2NNAa9j0WXQ93CuwIdosqGAAjDvTmEo55HCw2HM7fiF6oNZQOLpuLVpR80a7QSYEtgZ/8ubYSkG5206lx8nelW6O2rvcCGkJbfxL8+3Skl8IIUSPIoFO9Go6EGDjn/5M/VNP8fXeBdwwLcBff3g7Eysn5npo2fn4KVg405hCCeBdDy/MgFWPQd234F1nHO8/Ho76o1GJ6zdGplKKPVo4otncaIQ1Y82aLym41dT72drUmnZfkctKpcfJgCIXE4cWJ02BrPQ4qPA4sJpNOfhEQgghxM6TQCd6rdCWLVTPuhjfRx/x32MquOvA7dx0zC0cWnVoroeWvdcXtIW5mHArrHkTRp4AR1xq7BHnrsjN+ITYzbTWbGsOxLs/xitqsYYj9T42NbYSTtkdO89mNlr1FzrZt7LACGmFDqoS9mBz2qTJiBBCiL5HAp3olXyffEL1jIsIN3h58dcjeLz/Om488kYmD5yc66HtGG91OycUnPHEbh2KELtDgz/Ytr9atKq2ob7tca3XT2soed2azWKKhjIHBw0riQY3R1Ib/wKHRdatCSGE2CNJoBO9Tv3zz7Nx7jzMZaX8c+Y+vGT6lOsPu57jBh+X66HtmNYmY/1byJ9+zjNg949HiF3kD4bjm2BviFbWEqdD1tT7aWpNXrdmUtAvujn2mCoPx4+uiFfU+hcazUdKZHNsIYQQol0S6ESvoUMhNt94I9se+SfOSRO5/Sc2Xqt/n2sPvZYfDvthroe3Yxo3wWOnGmHObIVwwqbCVqfRGEWIHiQYjrCpwZ+015oR3Noqa9uaA2n3lebbqPQ4GVKSxyF7lcb3XoutXSt327HIujUhhBBip0mgE71CaPt2Nsy+hJb338fzP7/gxkmbWbLhTa4+6GpOGX5Kroe3Y7ashn//DFq2whlPgd+buculEF3g+ZUbuPHV1dTU++hf6OTyKaM4ZVxV0jWRiGZrc2t82mNbs5G2qZGbG/2kLFvD7bDEpz3+YGBhvH1/bDpkhceBwyrr1oQQQojupLTWnV+1G02YMEEvX74818MQPYh/9WqqL5xBaPNmyufP4/qS91m8djF/mPgHfrHPL3I9vB2z9r/wxOlgthlhrqqXbK0geqXnV25gznOf4AuG48esZsWRI8twO6zxvdY2eVsJhJPXrdktJqOhSGxD7Gjr/kpPtNFIoZN82RxbCCGE6BZKqRVa6wnZXCv/NRY9WsMrr1IzZw5mt5uB/3qE6xqfZvG3i7nkgEt6X5j75Bl4/gIoHAxnPgNFQ3I9ItFHhCOamnof67e1sG5bC99Hv7722UaC4eR/tAuGNUu+2ExVdNrjuIFFVO4X6wbpjE+JLHLJ5thCCCFEbyCBTvRIOhJhy623UXfvvTj335+qW2/lz9/exYvfvsiF+1/IWWPOyvUQs6c1LL0N/jMXBh0MP38MXMW5HpXoZRr9QdZta2kLbXUt8efV232EEuZDWkyKAUXOtDAXo4D//uHo3TRyIYQQQnQnCXSixwk3NlJz2eU0vf02haeeSvlVf+SGVTfx7NfPcs5+53D+D87P9RCzFwnD4ivgw/th9I/hlHvA6sj1qEQPFI5oNjb4WVdnhLTvtzWzbpsvHtpSG44UuqwMKnYxusrDD/erZFCxy/hV4qKiwIHFbOLQ699gQ70v7b36Fzp318cSQgghRDeTQCd6lNY131F94YUE1q+nYt5cPNOnc/NHN/PYl4/xq31/xUXjLsr1ELMXaIFnfwOrX4ZDLoJjF4BJuvntyZpbQ6zb1lZZS62yJa5jM5sUVYVOBhW7mDqmoi2wFbsYWOzC47R2+n6XTxmVtobOaTVz+ZRR3fL5hBBCCLH7SaATPUbjW29Rc9nlKJuNwQ89iOvAA7l95e08/NnD/HzUz7l0wqW9Z01P0xZ4fDps+AhOuBEmnZvrEYndIBLRbGo0qmyx4JYY4LY2JVfZ3A4Lg0tc7F3p5vjRbaFtcImLSo9jl9v5x7pZdtblUgghhBC9lwQ6kXNaa+ruvY8tt96KY599GHDH7Vj79+fe/7uX+z6+j5+O+ClzJs3pPWFu6zfw6E+NveZ+/ijsPS3XIxJdqCUQYn10KuS6bS2sq2tuC23bfQRCbVU2kzKmNw4qdnHcvv0YmFBlG1ych8fVeZVtV50yrkoCnBBCCNGHSaATORVpbqbmyj/S+OqrFPzoR1ReuwCT08nDnz7MHavu4MRhJzL34LmYVC+ZqrjufXj856BM8OuXYEBW3WZFD6K1ZnNjazSsGR0j1ydU2rY0tiZdn2+3MKjYxYhyN8fu0xbaBpe46F/oxCqbZgshhBCiG2UV6JRSU4FbATNwv9b6+pTzlwC/BULAFuBsrfX30XNh4JPopeu01id10dhFLxeorqb6dxfS+s03lF9xBcVn/RqlFI9+8Sh/X/F3pg6ZyoJDF/SeMPf5C/DsOeCpgl88AyV75XpEoh3+YDgppH1f1xba1m9vwR9sq7IpBf09TgYWOzl6VDmDSlxtoa3YRaG09xdCCCFEDnUa6JRSZuBO4DigGvhQKfWi1vrzhMtWAhO01i1KqQuAG4Dp0XM+rfX+XTxu0cs1L1vGhotno4GB991H/mGHAvD0V09z/QfXc/TAo7nu8OuwmHpJEXnZXfDqlTDgQDj9CcgryfWI9mhaa7Y0tbaFtjof329rjj/f1JBcZXPZzAwqdjG0NI/Jo8rijUcGFbuoKnJit5hz9EmEEEIIITqWzU/LE4FvtNZrAJRSTwAnA/FAp7V+M+H694Ezu3KQou/QWrP9n/9k0w03Yh82lAF33olt0CAAXvjmBa5ddi2HVx3OjUfeiNXU/euLdlkkDK/+Ef7f3bD3j+Cn94NVWsLvDv5gmOrtvoz7sq3b1pLU2VEpqChwMLDYxREjyuLt/QdGq2zFeTapsgkhhBCiV8om0FUB6xOeVwOTOrj+N8DihOcOpdRyjOmY12utn0+9QSl1LnAuwKDoD/ei74m0trJx7jy8L7yA+7hjqfzL9Zjz8wBY/N1i5i6dy6TKSdx81M3YzLYcjzYLQR88dw58sRAmXQBT/gwmqeR0Fa01dc2BtpAWXc8We76xwY9O2DfbaTXHK2uHjShNavE/oMiJwyq/N0IIIYToe7IJdJn+2VpnOIZS6kxgAnBkwuFBWusapdQw4A2l1Cda62+TXkzr+4D7ACZMmJDxtUXvFty4keqLZuL/5BNKL5pB6QUXoKJ7si35fglz3p3DuPJx3Hb0bdjN9hyPNgvNdUbzk+oPYcp1cPCFuR5Rr9QaCrNhuy9tX7bY8+ZAOOn6fgV2BhW7OGSvaGArcUaDWx6l+VJlE0IIIcSeJ5tAVw0MTHg+AKhJvUgpdSzwR+BIrXV8gYrWuib6dY1S6i1gHPBt6v2i72r56COqZ85Ct7Qw4M47cB9zTPzc2+vf5vJ3LmdM6RjuPOZOnJZeMF1x2xr498/AWw2nPgyjT8n1iHosrTXbW4Ipm2k3Rx/7qPH6kqpsdospXlk7eK+StM20pcomhBBCCJEsm0D3ITBCKTUU2AD8HDgj8QKl1DjgXmCq1npzwvEioEVr3aqUKgUOxWiYIvYQ2598io1/+hO2/v0Z8PBD2IcPj59bumEps9+azaiiUdx97N3kWfNyONIsVS+Hx6aDDsOvXoRBB+V6RDkXCEWoqfclb6SdsJ6tsTWUdH2Z26iyTRpa3LYvW4mxlq00347JJFU2IYQQQohsdRrotNYhpdQM4FWMbQse1Fp/ppRaACzXWr8I3AjkA09HpzzFtifYB7hXKRUBTBhr6D7P+EaiT9GBABuvu476J54k7/DDqfr73zAXFMTPf7jxQ2a+OZNhnmHce9y9uG3uHI42S18ugmd+A+5+8ItnoXR45/f0EfUtgYwt/tdta6Gm3kckocpms5gYWGRMhZyYGNqKXQwsduKy9ZLOpUIIIYQQvYDSumctWZswYYJevnx5rochdkFo61aqZ12Mb8UKSs75LWUXX4wyt02VW7l5Jef95zz65/XnwakPUuwozuFos/TBP+Dly6H/ODjjKcgvy/WIulQoHKGm3t8W2hJa/K+ra6HBn1xlK823Je3F1raZdh7lbqmyCSGEEELsCqXUCq31hGyulX8qF13K98mnVF90EeH6evr//W94pk1LOv/Jlk+4YMkF9HP14/4p9/f8MBeJwJJ5sPS2/9/enYdFWf19HH8fEBDRQMUVcMs10zTRFktLc2mx1bLUytL0Mc3MNG1fLZdKLZdSsX6V2l5PRuVui1bmlqW4kguLIiIg+zLn+QN+PVqCAwwM4Od1XVzM3HPOub8Tt118OHOfAy2vhf5h4F0BPhp6Bknp2QUu8R+dmE7uKdNsXp6GkJp5Qe3iRjVP25etUa1q+Pnofx0iIiIi5YF+KxOXSVq2jNinnsazdi2aLFlM1QsuOO31iOMRjFg1gpo+NVnYeyGBvoFuqtRJ2Rnw5UjY8Tl0HgbXTivX2xLk5DqITcr4/9CWcHpoS0zLPq19Lb+8WbaLQgK48aKGf4e2xrWrUe+8qnhqlk1ERESk3FOgkxKzOTnEvfoaCe++S7UuXQiaOYMqtU6fedt7Yi/DVw6nuld1wvqEUc+vnpuqdVJaAnw4CA5tgF4vwOVj8nanLgVfbo1m+vLdxCSm0zDAlwl9WnFzx6Aztj2ZkV3gEv9RJ9LJOWWWrYqHIbimLyG1qnFD+wanrRbZqFY1alStABu3i4iIiEihFOikRHITE4ke9yipGzZQc/Bg6k18DON1elCITIpk2IpheHt4E9Y7jIbVG7qpWiedOJC3LUHiQbgtDNr1L7VTfbk1msc//4P07Lz91qIT05n42Xb2xp2kcS0/Diakcijh//dpS0jNOq1/QDUvGtWqRtsgf65rd3poa+BflSqeHqVWu4iIiIi4nwKdFFvG7j1EjR5NzpEjNJg8mYDbbv1Xm8PJh3lg+QMYDAv7LCTkvJAzjFSORG/J25YgNxPu/gKaXFGqp5u+fPffYe6/MnMczFmbt1Wjp4chKCBvxci+F9b/175s/r6aZRMRERE5lynQSbEkr1hBzKTH8fTzo/H77+HbocO/2sSkxDB0xVCyHFks6rOIpv5N3VBpEexZDp8MgWqBMORrqNOqVE93JCmD6MT0Al//YcLVNAzQLJuIiIiIFEyBTorEOhzEz55N/Nx5+F50EUFvvIFXvbr/anck9QhDlw8lJTuFsN5htKjZwg3VFsGmdyB8HNRvl7ctQY36pXaq1Mwc3v4hkgU/RBbYJijAl0a1q5VaDSIiIiJSOSjQidNyU1KImfAYKWvX4n/brdR/9lk8vL3/1S4+PZ4HVjzAicwTLOi1gDa127ihWic5HLDmRfjpdWjRG/q/Az7VS+VUuQ7LZ5ujeHXFbuJOZnJD+wZ0bBTAq8v3nPaxS18vTyb0Kd3ZQRERERGpHBToxCmZf/1F1KjRZB06RL2nn6LmwIGYM6z6mJCRwLDlwziadpS3e71Nuzrt3FCtk3Ky4H9HwR8fw8X3wvWvg2fp/JNYvy+el8IjiIhNpmOjAOYN7kSnxjUBqO3n4/QqlyIiIiIip1Kgk7NK+f57osdPwFSpQqOwMPwu6XLGdkmZSQxfMZyolCjmXTOPjnU7lnGlRZCeCB8NhgM/Qo+n4cpHS2Vbgn1xKbzyTQSrd8URXNOXN+/qyA3tG5wWhm/uGKQAJyIiIiLFokAnBbLWcnzBQo7NmIFP69aEzH4Tr6AC9kfLOsmIlSOITIpkdo/ZdK7fuYyrLYLEw7D4dji+D26ZDxcNcPkpjqdkMnPVXpZsPEQ1L08ev7Y1917ehKpe5XdjchERERGpeBTo5IwcaWnEPvUUyd98y3nXXUeDyS/h4et7xrap2amMXDWS3Sd2M/OqmVwedHkZV1sEsdvzwlx2Ggz+DJp1d+nwGdm5vLvhAHPW7CMtO5dBlzTi4Z4tqF3dx6XnEREREREBBTo5g6yoaKJGjyZz927qjn+UWkOHnvF+OYD0nHRGrx7Nn/F/8mr3V+ke4tqA5FL7VsHH90JVf7h/OdS7wGVDW2v5enssU7/bRdSJdHq2rsvj17Wmed0aLjuHiIiIiMg/KdDJaVJ/+ZXosWOxDgch89+m+pVXFtg2MzeTMWvGsCVuC1OunMI1ja8pw0qLaMv7sOxhqNsGBn0C5zV02dCbD57gpfCdbD2USJsG57F4WHu6Ng902fgiIiIiIgVRoBMgb4bpxPsfcHTqVLybNCFkzmy8mzQpsH12bjaPrH2EX2N/5cWuL3Jt02vLrtiisBbWvQLfT4VmV8Md70HV81wy9OGENKZ8t4vw7bHUreHDtP7tue3iYDw9XL+4ioiIiIjImSjQCY7MTI489zxJX3xB9R49aDhtKp7VC96LLduRzfjvx/Nj9I88c9kz3NT8pjKstghysvJm5X5fAh0GQ7+Z4OlV4mGT0rOZu3Yf76w/gKeH4eGeLRjerRl+PvrnJCIiIiJlS7+BnuOyjx4l6qExZGzfTuCoUQSOehDj4VFg+xxHDo//+DhrDq9hUpdJ3N7y9jKstggykuHjuyFyHVz1OHSfWOJtCbJzHSzdeIgZK/eQmJ7NbRcHM753K+r7V3VNzSIiIiIiRaRAdw5L27qVqDFjsKlpBL35Buf16lVoe4d18Mz6Z1h+YDmPdnqUQW0GlVGlRZQck7eS5bFdcNMc6Di4RMNZa1mzK47J30QQeSyVy5rV5snr23BhkL+LChYRERERKR4FunNU4qefcuT5F6jSoAEhixbh06JFoe0d1sELP7/AsshljO4wmiEXDimbQovq6I68MJeRBAM/huY9SzTcjpgkJodHsGH/cZoF+rHwnlB6tqlb4KqfIiIiIiJlSYHuHGOzszn6yiucWLIUv65dCXr9NTz9C59pstbyyq+v8NnezxjefjgjLhpRRtUWUeQ6+Ohu8PaD+76FBu2LPdTR5AxeXb6bT7dEEeDrxfM3tmXgJY3w8iz446giIiIiImVNge4cknP8ONEPjyVt0yZqDb2fuuPGYTw9C+1jreXVTa/y4e4PGdJ2CKM7jC6jaoto21L4ajQEtszblsA/uFjDpGXlMP+HSN7+PpJch+WBK5sx6urm+PuWfDEVERERERFXU6A7R6Tv2EHU6IfITUig4fRp+Pfr51S/N7e+yXs732Ng64GM6zSu/H3U0Fr44VVY+xI0uRIGfAC+AUUeJtdh+WxLFK+t2M3R5Eyub9eAiX1b06h2tVIoWkRERETENRTozgFJX4cT++STeNaqReMli/Ft29apfm/9/hYL/lhA/5b9mdRlUvkLc7nZED4OtrwH7QfAjbOhineRh9mwL56XwiPYGZtMh5AA5g66mE6Na5VCwSIiIiIirqVAV4nZ3FziXn+dhLBFVAsNJWjWTKrUru1U30V/LmLOtjnceP6NPH3p0+UvzGWehE+GwL5VcOV46PFUkbcl2BeXwpRvI1gVEUdQgC9v3NWRfu0blL/3KiIiIiJSAAW6Sio3KYnocY+Sun49NQcOpN7jkzBezt0HtjhiMTM2z+DaJtfywuUv4GHK2UIgJ4/krWR5dAfcMBNC7ytS94TULGau2sPiXw9RzcuTiX1bc1/XJlT1Kvx+QhERERGR8kaBrhLK3LuXw6NGkx0bS/0XX6Dm7c5v/v3x7o+ZsnEKPRv1ZPKVk/H0KGchJ24XLO4PaQlw14fQsrfTXTNzcnl3/QFmr91HWlYuA7s0Yuw1Lahd3acUCxYRERERKT0KdJXMyVWriHlsIsavGo3/8x+qXdzR6b5f7vuSF395kW7B3ZjebTpeHuVsZccDP8GHA8HTB+4Lh4bOvTdrLeF/xDL1u10cTkinR+u6PHFda5rXrVHKBYuIiIiIlC4FukrCOhzEz5lL/Jw5VG3fnuA338CrXj2n+4dHhvPM+me4rMFlvH7V63h5lrMw98en8OVIqNkEBn0KNRs71W3LoRO89PVOthxKpHX9Gnww9BKuaBFYurWKiIiIiJQRBbpKIDcllZiJE0lZvRr/W26h/nPP4uHj/McIVx5cyZM/PUlo/VBm9ZiFj2c5+giitbB+Jqx6Dhp3hTsXg2/Ns3Y7nJDG1O928fX2WOrU8GHabe25rVMwnh5a8EREREREKg+nAp0xpi8wC/AEFlprp/zj9XHAMCAHOAbcb609mP/avcBT+U1fstb+x0W1C5B18CCHR40i668D1HviCWrePbhIqzSuO7yOx75/jHaB7ZjdYza+VXxLsdoiys2Bbx+DTWHQ9la45S2oUnjYTM7IZs7afbyz/gAeBsb0bMGIbs3w89HfLkRERESk8jnrb7nGGE9gDtALiAJ+M8Z8Za3deUqzrUCotTbNGDMSmAYMMMbUAp4FQgELbM7ve8LVb+RclPLjj0Q/Oh7j4UGjsIX4XXppkfqvj17PuHXjaF2rNXOvmUs1r3K0iXZWKnw6FPZ8C10fhp7PgUfBq23m5DpYuvEQM1bt5URaFrd2DGZ8n5Y08C9HAVVERERExMWcmbboAuyz1kYCGGM+BG4C/g501tq1p7T/BRic/7gPsNJam5DfdyXQF1ha8tLPXdZaEsLCiHt9Bj4tWhA8ZzbewcFFGmNj7EYeXvsw5wecz1u93qKGdzlaICQlDpYMgNhtcN2r0OWBAptaa1m7O47J4RHsP5bKpc1q8dT1F3BhkH8ZFiwiIiIi4h7OBLog4PApz6OASwppPxT4tpC+Qf/sYIwZDgwHaNSokRMlnbsc6enEPvU0yeHh1Li2Lw0nT8ajWtFm1rYc3cLoNaMJqRHC/F7z8fcpR+Enfi98cFteqBuwGFpfV2DTnTHJTP5mJ+v3HadZoB8L7gnlmjZ1tTG4iIiIiJwznAl0Z/rt2J6xoTGDyft4Zfei9LXWzgfmA4SGhp5xbIHs6GgOj36IzF27qDNuHLUfGFbk8LL92HYeXP0g9arVY0HvBdSsevYFRsrMoV9g6Z1gPGFIOAR3OmOzo8kZvLZiN59sjsLf14vn+l3AoEsb4+VZzjZAFxEREREpZc4Euigg5JTnwUDMPxsZY64BngS6W2szT+l71T/6ritOoee61I0biX54LDYnh5C35lG9e/ezd/qHncd38j8r/4eaPjVZ2Hshgb7laPn+HV/C58PBPxgGfwq1mv2rSVpWDgt++Iu3vt9PjsPBsCuaMvrqFvhXK2dbLIiIiIiIlBFnAt1vQAtjTFMgGrgTGHhqA2NMR+BtoK+1Nu6Ul5YDLxtj/jsN1Bt4vMRVn0OstZxYsoSjr0zBu1EjgufMxqdp0yKPs+fEHkasHEF17+qE9Qmjnp/ze9SVKmvh5zmw4ikI6QJ3LgW/2qc1cTgsn2+NZvryXRxNzuS6dvWZ2Lc1jWv7ualoEREREZHy4ayBzlqbY4wZTV448wQWWWt3GGNeADZZa78CpgPVgU/yPwJ4yFp7o7U2wRjzInmhEOCF/y6QImfnyMriyPPPk/TZ51S/+moaTp+GZ/XqRR4nMimSB1Y8gLenN2G9w2hYvWEpVFsMjlxY/gT8+ha0uRFunQ9ep69KuWF/PJPDI9gRk8xFIQHMGXgxoU1qualgEREREZHyxVhbvm5ZCw0NtZs2bXJ3GW6XfTSO6DFjSP/9d2qP/B/qPPQQppBl+wtyKPkQQ74bgsM6eKfvOzT1L/rsXqnITofPhsGur+HSUdD7pdO2Jdh/LIVXvtnFqoijBAX48ljfVvRr3xAPbQwuIiIiIpWcMWaztTbUmbbabbkcSt+2jaiHxpCbmkrQrFmc16d3scaJTolm6Iqh5DhyWNRnUfkJc6nxeYufRG2CvlPg0pF/v5SQmsUbq/fywS8HqerlyWN9W3F/16ZU9fJ0Y8EiIiIiIuWTAl05k/jZ5xx57jmq1K9Pk4ULqdqqZbHGOZJ6hKHLh5KWnUZYnzCa12zu4kqL6fh+WNwfkmPgjv/ABTcBkJmTy382HODNNftIzcxh4CWNGHtNSwKr+7i5YBERERGR8kuBrpyw2dkcnTqNEx98gN/llxH0+ut4BgQUa6xjaccYtmIYSZlJLOi9gNa1Wru42mI6/BssHZC3EMo9X0GjS7DW8s0fR5jyXQSHE9K5ulUdnriuDS3qlaONzkVEREREyikFunIgJyGB6LGPkLZxI7WGDKHu+EcxVYr3ozmefpxhK4YRlxbH/F7zuTDwQhdXW0wRy/LumavRAAZ/BrXPZ+uhE7wUHsHmgydoXb8G7w/twpUt6ri7UhERERGRCkOBzs0yIiKILZuUJQAAGf9JREFUGjWanPh4Gk6dgv9NNxV7rKTMJIavHE5MSgxzr5lLh7odXFhpCfz6Nnw7EYIuhrs+4nCWH9OWbmXZ7zHUqeHDlFvbcXtoCJ5a8EREREREpEgU6NwoKTyc2CefwjMggMaLF+PbrvizaclZyQxfOZwDSQd4s+ebdK7f2YWVFpPDASufhp9nQ6vrSb5hHnN/jGXR+k14GBjTozkjup+Pn48uQxERERGR4tBv0m5gc3M5NmMGxxeG4dupE8GzZlIlMLDY46VmpzJy1Uj2nNjDrKtncXnDy11YbTFlZ8AXI2DnlzhCh7Gk9ihmzNzI8dQsbr04iAl9WtHA3/fs44iIiIiISIEU6MpYblIS0eMnkPrjjwTcOYD6TzyB8fYu9nhp2Wk8uOpBdsTv4LXur9EtuJsLqy1uUQmw9C44/At7O0xk5O7L2Xcsgkua1uLd6y+gXbC/uysUEREREakUFOjKUOa+fUSNGk1WTAz1n3+emgPuKNF4GTkZjFk7hm3HtjH1yqn0bNzTRZWWwIkD8EF/HCcOMqfWE7z2y4U0DYT5d3ei1wX1MEb3yYmIiIiIuIoCXRk5uXo1MY9NxPj60vjdd6jWqVOJxsvKzeKRdY+wMXYjk6+YTN+mfV1UaQlEb8Gx+HYyMjMZkjGJPYnteLZfCwZd0hjvKh7urk5EREREpNJRoCtl1uEgft484t+cTdULLyR49pt41a9fojGzHdmM/348P0X/xLOXPUu/8/u5qNriy9gRjudn93M0twbDcp7lisu7sqBHC/yrebm7NBERERGRSkuBrhTlpqQS+/gkTq5chf9NN1L/+efxqFq1RGPmOHKY9MMk1h5ey+NdHqd/y/4uqrZ4HA7Lti9e56I/XmKHozGLm03n7X6X07i2n1vrEhERERE5FyjQlZKsgweJGj2azMi/qPf4JGrec0+J7x/LdeTy9PqnWXFwBeNDxzOwzUAXVVs8G/bFEfXp49yR8Sm/eXXG4453mNoixK01iYiIiIicSxToSkHKT+uJHjcOYwyNFi7A77LLSjymwzp44ZcX+Drya8Z0HMO9be91QaXFs/9YCtPDt9N3/4vc4bmBv5rcQafBc/Gooo9XioiIiIiUJQU6F7LWkrDoHeJeew2f5s0JnjMb75CSz1hZa3n515f5fO/njGg/ggfaP+CCaovuRGoWs1bv5atfdvC21ww6e+4k+6qnadr9UdDqlSIiIiIiZU6BzkUcGRnEPv0MycuWUaNPHxq+PBkPv5LfR2atZfqm6Xy0+yPua3sfozqMckG1RZOZk8t7Gw7y5pq9nJcZy7fnzaBudjTcvACv9iXbekFERERERIpPgc4FsmNiiBr9EBkREdQZ+zC1R4xwyX5r1lre2PoG7+98n0FtBvFIp0fKdB83ay3f/nmEKd/u4lBCGvc0TeKZpMlUyc2Euz+HpuVgE3MRERERkXOYAl0Jpf32G1EPj8VmZRE8dw41rr7aZWO/tf0tFv6xkNtb3s7EzhPLNMxtPXSCyeERbDp4glb1arCsbzrtNkyAqgEw5Cuo26bMahERERERkTNToCsmay0nli7l6Muv4B0SQvCc2fg0a+ay8cP+CGPutrncdP5NPHXpU2UW5qJOpDHtu9189XsMgdV9eOXWdgzwXIvH149AvQtg4CdwXoMyqUVERERERAqnQFcMjqwsjr74IomffEr17t1p+Op0PGvUcNn47+98n5lbZnJt02t5/vLn8TAeLhu7ICczspm7bj9hP/2FAR7q0ZwR3ZpRfcM0+GEanN8T7vgP+LjufYqIiIiISMko0BVRdlwc0Q+PJX3rVmqPGEGdMQ9hPD1dNv5Huz5i2m/T6NW4Fy9f8TKeHq4b+0xych18+NthZqzcw/HULG7tGMT4Pq1oWN0Tlj0Evy+FjoPhhpngqW0JRERERETKEwW6s0hatoy4GTPJiY3Fs3ZtHJmZkJND0MwZnNe3r0vP9cXeL3jp15foHtydqVdOpYpH6f14rLWs232Ml7+JYG9cCl2a1uKd69vQPjgAMpJgyT0QuQ6uegK6P6ZtCUREREREyiEFukIkLVtG7NPPYDMyAMiNjwdjCBz7sMvDXHhkOM9ueJbLG17Oa1e9hlcpzoZFxCbz8jcR/Lg3nia1q/H23Z3ofUG9vPv0kqJh8e0QvxtungcdBpZaHSIiIiIiUjIKdIWImzHz7zD3N2tJ/Ohj6owY4bLzrDiwgid/epLO9Tsz8+qZ+Hj6uGzsU8WdzOD1FXv4eNNhalT14pkbLmDwpY3xrpJ/j96RP/PCXOZJGPQJnN+jVOoQERERERHXUKArRE5sbJGOF8faQ2uZ+MNE2tdpz5s93sS3iq/Lxv6v9KxcFvwYyVvf7yc718F9XZvyUI/mBFTz/v9G+9fAR/fkLXpy/3dQ/0KX1yEiIiIiIq6lQFeIKg0akBMTc8bjrvBT9E88+v2jtKndhrk951LNq5pLxv0vh8PyxdZopi/fzZHkDPq2rc+ka1vTJNDv9IbblsBXD0Fgq7yZOf8gl9YhIiIiIiKlQ4GuEHUfGXvaPXQApmpV6j4ytsRj/xr7K2PXjqV5QHPmXTOP6t7VSzzmqX6JPM5L4Tv5MzqZ9sH+vHFXR7o0rXV6I2vhh+mwdjI07Q4D3oeq/i6tQ0RERERESo8CXSH8+/UD+HuVyyoNGlD3kbF/Hy+uzUc389CahwipEcLbvd7G38d1ISryWAqvfLuLlTuP0tC/KjMHdODGixri4fGPVSpzs+HrR2Dr+3DRXdDvDajifeZBRURERESkXFKgOwv/fv1KHOBO9fux33lw1YPUq1aPBb0XULNqTZeMeyI1i1mr9/LBLwfxqeLBhD6tGHpFU6p6nWEfu8yT8PG9sH81dHsMrn5C2xKIiIiIiFRATgU6Y0xfYBbgCSy01k75x+vdgJlAe+BOa+2np7yWC/yR//SQtfZGVxReEe08vpORK0dS27c2C3svJNA3sMRjZubk8v7PB3lj9V5SMnMY0LkR43q1pE6NAlbKTI6FJbfD0Z15s3Kd7i1xDSIiIiIi4h5nDXTGGE9gDtALiAJ+M8Z8Za3deUqzQ8AQYPwZhki31nZwQa0V2u6E3QxfOZwa3jUI6x1GPb96JRrPWst3fx7hlW93cSghjW4t6/DkdW1oVb9GwZ3iIvK2JUhLgIEfQYteJapBRERERETcy5kZui7APmttJIAx5kPgJuDvQGetPZD/mqMUaqzwIhMjGb5yOD6ePizss5AG1Uu2Sua2w4lMDt/JbwdO0LJedf5zfxe6t6xTeKe/foAPB4NXVbjvG2h4zmdsEREREZEKz5lAFwQcPuV5FHBJEc5R1RizCcgBplhrv/xnA2PMcGA4QKNGjYowdPl3MPkgw1YMw2AI6x1GSI2QYo8VnZjOtO928b/bYgis7s3Lt7TjjtBgqnh6FN5x+yfw5Uio1QwGfwoBleu/sYiIiIjIucqZQHem1TJsEc7RyFobY4xpBqwxxvxhrd1/2mDWzgfmA4SGhhZl7HIt6mQUQ5cPJceRw6I+i2ji36RY45zMyGbeuv0s/OkvDDDq6vMZeVVzqvuc5cdnLfz0Oqx+ARpfAXd+AL6uWYRFRERERETcz5lAFwWcOq0UDPx7t+0CWGtj8r9HGmPWAR2B/YV2qgSOpB5h2IphpOeks6jPIprXbF7kMXJyHXy06TAzVu4hPiWLWzoGMb5PK4ICfM/eOTcHvp0AmxbBhf3h5rlQpYCFUkREREREpEJyJtD9BrQwxjQFooE7gYHODG6MqQmkWWszjTGBQFdgWnGLrSiOpR1j6PKhJGUmsbD3QlrValXkMdbtjmNyeAR741Lo0qQWYfe24aKQAOc6Z6bAp/fD3uXQdSz0fBY8zvKxTBERERERqXDOGuistTnGmNHAcvK2LVhkrd1hjHkB2GSt/coY0xn4AqgJ9DPGPG+tbQu0Ad7OXyzFg7x76HYWcKpK4Xj6cYatGMax9GPM7zWftoFti9R/15FkJodH8OPeeBrXrsZbgy+mT9v6GGf3iTt5FJbcAUe2w/WvQedhxXgXIiIiIiJSERhry9cta6GhoXbTpk3uLqNYEjMSuX/F/RxOPsy8a+YRWj/U6b5xJzOYsXIPH/12mBpVvRjTswV3X9oY7ypFmFk7tgcW3wap8dD/HWjVtxjvQkRERERE3MkYs9la61SYcGpjcTm75Kxkhq8czsGkg8zuOdvpMJeelUvYT5HMW7efzBwHQy5vypiezQmo5l20Ag7+DEvvBE8vGPI1BHUqxrsQEREREZGKRIHOBVKzUxm5ciR7E/cy6+pZXNbwsrP2cTgsX26LZvry3cQmZdCnbT0mXduGpoF+RS/gz8/hi/+BgBAY9CnUalqMdyEiIiIiIhWNAl0JpWWn8eCqB9l5fCevXvUq3YK7nbXPr5HHeSk8gj+ik2gX5M/MAR24pFntop/cWtjwJqx8GkIuhbuWQrVaxXgXIiIiIiJSESnQlUBGTgZj1oxh27FtTO02lZ6Nehba/q/4VKZ8G8HyHUdp4F+VGQMu4qaLgvDwcHLBk1M5cuG7SbBxPlxwM9zyNnhVLeY7ERERERGRikiBrpiycrMYu24sG49sZPIVk+nbpOAFSBLTspi1ei/v/3wQnyoejO/dkqFXNMPX27OYJ0+Dzx+AXV/DZaOh14valkBERERE5BykQFcM2Y5sHv3+UdZHr+e5y56j3/n9ztguK8fBez8f4M01+ziZkc2AziE80qsldWuUYCYtNR6WDIDozXDtNLhkRPHHEhERERGRCk2BrohyHDlM/GEi6w6v44lLnuC2lrf9q421luU7jjDl210cOJ7GlS0CefL6NrSuf17JTn58P3xwG5yMhQHvQ5szB0kRERERETk3KNCdRXhkOLO2zOJI6hHq+9Wnrm9dfo//nQmhE7ir9V3/av/74UQmh0ew8UACLetV5937OnNVq7olL+TwxryZOWPg3q8hpHPJxxQRERERkQpNga4Q4ZHhPLfhOTJyMwCITY0lNjWWPo37cE/be05rG52YzvTvdvHlthgCq3sz+ZYLGRAaQhVPF9zbFrEMPhsG5zXM25ag9vklH1NERERERCo8BbpCzNoy6+8wd6rt8dv/fpySmcO8dftY+ONfWODBq85n5FXnU6Oql2uK+OWtvNUsg0Phrg/BL9A144qIiIiISIWnQFeII6lHCjyek+vg401RvL5yN/EpWdzcoSET+rYmKMDXNSd3OGDFU/DLHGh9A9y6ALyruWZsERERERGpFBToClHfrz6xqbH/Oh7gXYfr3viRPUdT6NykJgvv7UyHkADXnTg7HT4fDhFfQZcR0PcV8CjmFgciIiIiIlJpKdAVomutu/nk5AyMR/bfx6zDi5i/ribIy8G8QRfT98L6GFOMjcELkpYAS++Ew79C78lw2ai8hVBERERERET+QYGuECs2BpHhuBWfOssxXonY7AAyj/XBL7szKx7rhk8VF8+aJfwFi/tD4mG4/V1oe4trxxcRERERkUpFga4QMYnpWDqSk9zxtOPJ5Lg+zEVthiV3gM2Fe/4XGl/m2vFFRERERKTSccGa+pVXwwIWOCnoeLHt+gbevR68/WDoSoU5ERERERFxigJdISb0aYWv1+kzcb5enkzo08p1J9m4AD4aBHVbw7BVENjCdWOLiIiIiEilpo9cFuLmjkEATF++m5jEdBoG+DKhT6u/j5eIwwGrn4P1s6BlX+i/KG+GTkRERERExEkKdGdxc8cg1wS4U+Vkwpcj4c/PIHQoXDsNPPWjEBERERGRolGKKGvpJ+DDQXBwPVzzHHQdq20JRERERESkWBToylLiIfigP5z4C24Lg3b93V2RiIiIiIhUYAp0ZSVmW962BDkZcPcX0OQKd1ckIiIiIiIVnFa5LAt7V8I714GnN9y/QmFORERERERcQoGutG1+F5YMgNrn5+0xV7e1uysSEREREZFKQh+5LC3WwpqX4MdXofk1cPu74FPD3VWJiIiIiEglokBXGnKy4KuHYPuHcPE9cP3r4Onl7qpERERERKSSUaBztYwk+Ggw/PUD9HgKrhyvbQlERERERKRUKNC5UlIULL4d4vfALW/DRXe6uyIREREREanEnFoUxRjT1xiz2xizzxgz6QyvdzPGbDHG5Bhj+v/jtXuNMXvzv+51VeHlzpE/YOE1eaFu8GcKcyIiIiIiUurOOkNnjPEE5gC9gCjgN2PMV9banac0OwQMAcb/o28t4FkgFLDA5vy+J1xTfjmxfw18dE/eoif3fwf12rq7IhEREREROQc4M0PXBdhnrY201mYBHwI3ndrAWnvAWrsdcPyjbx9gpbU2IT/ErQT6uqDu8mPr4ryPWdZsDMNWKcyJiIiIiEiZcSbQBQGHT3kelX/MGSXpW75ZC+umwP8+mLdR+H3fgn/leGsiIiIiIlIxOLMoypmWaLROju9UX2PMcGA4QKNGjZwc2o1ys2HZWNj2AVw0EPrNgire7q5KRERERETOMc7M0EUBIac8DwZinBzfqb7W2vnW2lBrbWidOnWcHNpNMpJhyR15Ya77RLh5rsKciIiIiIi4hTMzdL8BLYwxTYFo4E5goJPjLwdeNsbUzH/eG3i8yFW60/aPYfULeatX1mgAxgNOxsKNs+Hiu91dnYiIiIiInMPOGuistTnGmNHkhTNPYJG1docx5gVgk7X2K2NMZ+ALoCbQzxjzvLW2rbU2wRjzInmhEOAFa21CKb0X19v+MSwbA9npec9P5k8udn1YYU5ERERERNzOWOvs7XBlIzQ01G7atMndZeSZcSEkHf73cf8QeOTPsq9HREREREQqPWPMZmttqDNtndpY/JyVFFW04yIiIiIiImVIga4w/sFFOy4iIiIiIlKGFOgK0/MZ8PI9/ZiXb95xERERERERN1OgK0z7O6DfG3n3zGHyvvd7I++4iIiIiIiImzmzbcG5rf0dCnAiIiIiIlIuaYZORERERESkglKgExERERERqaAU6ERERERERCooBToREREREZEKSoFORERERESkglKgExERERERqaCMtdbdNZzGGHMMOOjuOs4gEIh3dxFSqekak9Kk60tKk64vKU26vqQ0ldfrq7G1to4zDctdoCuvjDGbrLWh7q5DKi9dY1KadH1JadL1JaVJ15eUpspwfekjlyIiIiIiIhWUAp2IiIiIiEgFpUDnvPnuLkAqPV1jUpp0fUlp0vUlpUnXl5SmCn996R46ERERERGRCkozdCIiIiIiIhWUAp2IiIiIiEgFpUDnBGNMX2PMbmPMPmPMJHfXI5WLMWaRMSbOGPOnu2uRysUYE2KMWWuMiTDG7DDGPOzumqRyMcZUNcZsNMb8nn+NPe/umqTyMcZ4GmO2GmO+dnctUrkYYw4YY/4wxmwzxmxydz3FpXvozsIY4wnsAXoBUcBvwF3W2p1uLUwqDWNMNyAFeM9ae6G765HKwxjTAGhgrd1ijKkBbAZu1v+/xFWMMQbws9amGGO8gJ+Ah621v7i5NKlEjDHjgFDgPGvtDe6uRyoPY8wBINRaWx43FneaZujOrguwz1obaa3NAj4EbnJzTVKJWGt/ABLcXYdUPtbaWGvtlvzHJ4EIIMi9VUllYvOk5D/1yv/SX4rFZYwxwcD1wEJ31yJSXinQnV0QcPiU51HoFyIRqWCMMU2AjsCv7q1EKpv8j8NtA+KAldZaXWPiSjOBxwCHuwuRSskCK4wxm40xw91dTHEp0J2dOcMx/fVRRCoMY0x14DNgrLU22d31SOVirc211nYAgoEuxhh9dFxcwhhzAxBnrd3s7lqk0upqrb0YuBYYlX8bTIWjQHd2UUDIKc+DgRg31SIiUiT59zV9Biy21n7u7nqk8rLWJgLrgL5uLkUqj67Ajfn3OX0I9DDGfODekqQysdbG5H+PA74g71arCkeB7ux+A1oYY5oaY7yBO4Gv3FyTiMhZ5S9YEQZEWGtfd3c9UvkYY+oYYwLyH/sC1wC73FuVVBbW2settcHW2ibk/f61xlo72M1lSSVhjPHLXzAMY4wf0BuokCuOK9CdhbU2BxgNLCdvQYGPrbU73FuVVCbGmKXAz0ArY0yUMWaou2uSSqMrcDd5f9Xelv91nbuLkkqlAbDWGLOdvD+ArrTWaml5EakI6gE/GWN+BzYC4dba79xcU7Fo2wIREREREZEKSjN0IiIiIiIiFZQCnYiIiIiISAWlQCciIiIiIlJBKdCJiIiIiIhUUAp0IiIiIiIiFZQCnYiIVFrGmNxTtmzYZoyZ5MKxmxhjKuSeRSIiUnlUcXcBIiIipSjdWtvB3UWIiIiUFs3QiYjIOccYc8AYM9UYszH/q3n+8cbGmNXGmO353xvlH69njPnCGPN7/tfl+UN5GmMWGGN2GGNWGGN83famRETknKRAJyIilZnvPz5yOeCU15KttV2A2cDM/GOzgfeste2BxcAb+cffAL631l4EXAzsyD/eAphjrW0LJAK3lfL7EREROY2x1rq7BhERkVJhjEmx1lY/w/EDQA9rbaQxxgs4Yq2tbYyJBxpYa7Pzj8daawONMceAYGtt5iljNAFWWmtb5D+fCHhZa18q/XcmIiKSRzN0IiJyrrIFPC6ozZlknvI4F92bLiIiZUyBTkREzlUDTvn+c/7jDcCd+Y8HAT/lP14NjAQwxngaY84rqyJFREQKo78kiohIZeZrjNl2yvPvrLX/3brAxxjzK3l/3Lwr/9gYYJExZgJwDLgv//jDwHxjzFDyZuJGArGlXr2IiMhZ6B46ERE55+TfQxdqrY13dy0iIiIloY9cioiIiIiIVFCaoRMREREREamgNEMnIiIiIiJSQSnQiYiIiIiIVFAKdCIiIiIiIhWUAp2IiIiIiEgFpUAnIiIiIiJSQf0fx/aS4NW18HgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "\n",
    "AdaGrad, like Adam, is a per-parameter optimization method that uses the following update rule:\n",
    "\n",
    "```\n",
    "cache += dw**2\n",
    "w += - learning_rate * dw / (np.sqrt(cache) + eps)\n",
    "```\n",
    "\n",
    "John notices that when he was training a network with AdaGrad that the updates became very small, and that his network was learning slowly. Using your knowledge of the AdaGrad update rule, why do you think the updates would become very small? Would Adam have the same issue?\n",
    "\n",
    "\n",
    "## Answer: \n",
    "In adagrad with iteration begins,cache became large,even if dw also very large,the division operation will lead w change very slowly.Adam don't have the same issus,because in numerator will became large at the same time. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with learning rate 0.000310 weight_scale 0.025000 \n",
      "[0.131, 0.428, 0.452, 0.497, 0.502, 0.537, 0.534, 0.572, 0.619, 0.609, 0.615, 0.63, 0.626, 0.654, 0.675, 0.704, 0.679, 0.707, 0.721, 0.743, 0.737, 0.763, 0.798, 0.789, 0.818, 0.808, 0.825, 0.816, 0.83, 0.872, 0.88]\n",
      "[0.092, 0.449, 0.451, 0.474, 0.494, 0.504, 0.5, 0.538, 0.532, 0.538, 0.517, 0.532, 0.545, 0.554, 0.547, 0.543, 0.559, 0.56, 0.554, 0.546, 0.543, 0.546, 0.551, 0.56, 0.55, 0.572, 0.562, 0.566, 0.568, 0.558, 0.564]\n"
     ]
    }
   ],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# find batch/layer normalization and dropout useful. Store your best model in  #\n",
    "# the best_model variable.                                                     #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "learning_rates = [3.1e-4]\n",
    "weight_scales=[2.5e-2]\n",
    "for learning_rate in learning_rates:\n",
    "    for weight_scale in weight_scales:\n",
    "        print('running with learning rate %f weight_scale %f '%(learning_rate,weight_scale))\n",
    "        model = FullyConnectedNet([600, 500, 400, 300, 200, 100], dtype=np.float64,reg=1e-2, weight_scale=weight_scale)\n",
    "\n",
    "        solver = Solver(model, data,\n",
    "                      num_epochs=30, batch_size=100,\n",
    "                      update_rule='adam',\n",
    "                      optim_config={\n",
    "                        'learning_rate': learning_rate\n",
    "                      },lr_decay=0.9,\n",
    "                      verbose=False)\n",
    "        solver.train()\n",
    "        best_model=model\n",
    "        print(solver.train_acc_history)\n",
    "        print(solver.val_acc_history)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test your model!\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.572\n",
      "Test set accuracy:  0.573\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  }
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